@article {808,
	title = {A distributed evolutionary fuzzy system-based method for the fusion of descriptive emerging patterns in data streams},
	journal = {Information Fusion},
	year = {In Press},
	note = {PID2019-107793GB-I00},
	doi = {https://doi.org/10.1016/j.inffus.2022.10.028},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {807,
	title = {Clustering: An R library to facilitate the analysis and comparison of cluster algorithms},
	journal = {Progress in Artificial Intelligence},
	year = {In Press},
	note = {PID2019-107793GB-I00},
	author = {L.A. P{\'e}rez-Martos and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona}
}
@conference {811,
	title = {A case of study with the Clustering R library to measure the quality of cluster algorithms},
	booktitle = {International Conference on Hybrid Artificial Intelligence Systems (HAIS) - Salamanca 5-8 septembre},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {88-97},
	author = {L.A. P{\'e}rez-Martos and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona}
}
@article {813,
	title = {DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework},
	journal = {The R Journal},
	volume = {14},
	number = {4},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {121-140},
	doi = {https://doi.org/10.32614/RJ-2023-002},
	author = {S{\'a}ez-Castillo, Antonio J. and Conde-S{\'a}nchez, Antonio and Mart{\'\i}nez, Francisco}
}
@conference {810,
	title = {Gamificaci{\'o}n mediante juegos de bloques en asignaturas del {\'a}mbito de la Inteligencia Artificial en el Grado en Ingenier{\'\i}a Inform{\'a}tica},
	booktitle = {VI Congreso Internacional sobre Innovaci{\'o}n Pedag{\'o}gica y Praxis Educativa (INNOVAGOG{\'I}A 2022) - 25 al 27 de mayo},
	year = {2022},
	address = {Madrid},
	author = {C. J. Carmona and P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico}
}
@article {812,
	title = {Strategies for time series forecasting with generalized regression neural networks},
	journal = {Neurocomputing},
	volume = {491},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {509-521},
	doi = {https://doi.org/10.1016/j.neucom.2021.12.028},
	author = {Francisco Mart{\'\i}nez and Francisco Charte and Mar{\'\i}a Pilar Fr{\'\i}as and Ana Mar{\'\i}a Mart{\'\i}nez-Rodr{\'\i}guez}
}
@article {803,
	title = {Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series},
	journal = {IEEE Access},
	volume = {10},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {3275-3283},
	doi = {10.1109/ACCESS.2022.3140377},
	author = {Mart{\'\i}nez del Rio, Francisco and M.P. Fr{\'\i}as and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@conference {809,
	title = {Uso de metodolog{\'\i}as de aprendizaje invertido para resoluci{\'o}n de la parte pr{\'a}ctica en asignaturas del grado de ingenier{\'\i}a inform{\'a}tica},
	booktitle = {XX Congreso Internacional de Investigaci{\'o}n Educativa (14-17 de junio)},
	year = {2022},
	address = {Santiago de Compostela},
	author = {C. J. Carmona and P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico}
}
@article {797,
	title = {A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams},
	journal = {Expert Systems with Applications},
	volume = {183},
	year = {2021},
	note = { PID2019-107793GB-I00, DOC 00235},
	pages = {115419},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {805,
	title = {An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects},
	journal = {Sensors},
	volume = {21},
	number = {19},
	year = {2021},
	pages = {6461},
	keywords = {Fuzzy markup language},
	doi = {10.3390/s21196461},
	url = {http://hdl.handle.net/10481/71361},
	author = {Mart{\'\i}nez Rojas, Mar{\'\i}a and M. J. Gacto and Vitiello, Autilia and Acampora, Giovanni and Soto Hidalgo, Jos{\'e} Manuel}
}
@article {781,
	title = {ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders},
	journal = {Information Sciences},
	volume = {565},
	year = {2021},
	note = {TIN2015-68454-R; PID2019-107793GB-I00 / AEI /10.13039/501100011033},
	pages = {146-176},
	abstract = {High dimensionality is an issue that affects most classification algorithms. This factor implies that the predictive performance of many traditional classifiers decreases considerably as the number of features increases. Therefore, there are numerous proposals that try to mitigate the effects of this issue. This study proposes ClEnDAE, a new classifier based on ensembles whose components incorporate denoising autoencoders (DAEs) to reduce the dimensionality of the input space. On the one hand, the use of ensembles improves the predictive performance by using several components that work jointly. On the other hand, the use of DAEs allows a new higher-level, smaller-sized feature space to be generated, reducing high dimensionality effects. Finally, an experimentation is conducted with the goal of evaluating the behavior of ClEnDAE. The first part of the test compares the performance of ClEnDAE to a model based on basic DAE and to the original untreated data. The second part analyzes the results of ClEnDAE and other traditional methods of dimensionality reduction in order to determine the improvement achieved with the proposed algorithm. In both parts of the experimentation, conclusions show that ClEnDAE offers better predictive performance than the other analyzed models. The main advantage of the ClEnDAE method is the combination of the potential of the ensemble-based methodology, where several components work in parallel, and DAEs, which generate new low-dimensional features that provide more relevant information. Therefore, the classification performance is better than with other classic proposals.},
	keywords = {classification, Deep learning, Denoising autoencoders, Dimensionality reduction, Ensembles feature fusion},
	doi = {https://doi.org/10.1016/j.ins.2021.02.060},
	author = {Pulgar, Francisco J. and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {800,
	title = {Clustering: Un paquete R para facilitar el an{\'a}lisis de algoritmos de agrupamiento},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	author = {L.A. P{\'e}rez-Martos and P. Gonz{\'a}lez and C. J. Carmona}
}
@article {792,
	title = {Decomposition-Fusion for Label Distribution Learning},
	journal = {Information Fusion},
	volume = {66},
	year = {2021},
	note = {TIN2017-89517-P},
	month = {02/2021},
	pages = {64-75},
	abstract = {Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. However, LDL is a generalization of the classification task and as such it is exposed to the same problems as standard classification algorithms, including class-imbalanced, noise, overlapping or irregularities. The purpose of this paper is to mitigate these effects by using decomposition strategies. The technique devised, called Decomposition-Fusion for LDL (DF-LDL), is based on one of the most renowned strategy in decomposition: the One-vs-One scheme, which we adapt to be able to deal with LDL datasets. In addition, we propose a competent fusion method that allows us to discard non-competent classifiers when their output is probably not of interest. The effectiveness of the proposed DF-LDL method is verified on several real-world LDL datasets on which we have carried out two types of experiments. First, comparing our proposal with the base learners and, second, comparing our proposal with the state-of-the-art LDL algorithms. DF-LDL shows significant improvements in both experiments.},
	keywords = {Decomposition strategies, Label Distribution Learning, machine learning, One vs. One},
	doi = {https://doi.org/10.1016/j.inffus.2020.08.024},
	author = {Gonzalez, M and Germ{\'a}n Gonz{\'a}lez-Almagro and Triguero, Isaac and J. R. Cano and Garc{\'\i}a, Salvador}
}
@conference {799,
	title = {E2PAMEA: un algoritmo evolutivo para la extracci{\'o}ni eficiente de patrones emergentes difusos en entornos big data},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	author = {A.M. Garc{\'\i}a-Vico and D. Elizondo and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona}
}
@article {793,
	title = {Enhancing instance-level constrained clustering through differential evolution},
	journal = {Applied Soft Computing},
	volume = {108},
	number = {107435},
	year = {2021},
	note = {TIN2017-89517-P; PP2019.PRI.I.06.},
	pages = {1-19},
	abstract = {Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it received renewed attention when it was shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information encoded by constraints. Constraints can be given in the form of instance-level must-link and cannot-link constraints, which this paper focuses on. We propose the first application of Differential Evolution to the constrained clustering problem, which has proven to produce a better exploration{\textendash}exploitation trade-off when comparing with previous approaches. We will compare the results obtained by this proposal to those obtained by previous nature-inspired techniques and by some of the state-of-the-art algorithms on 25 datasets with incremental levels of constraint-based information, supporting our conclusions with the aid of Bayesian statistical tests.},
	keywords = {Cannot-link, constrained clustering, Differential evolution, Instance-level, Must-link},
	doi = {https://doi.org/10.1016/j.asoc.2021.107435},
	author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
@conference {798,
	title = {FEPDS: Una propuesta para la extracci{\'o}n de patrones emergentes difusos en flujos continuos de datos},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	note = {DOC_0235},
	author = {A.M. Garc{\'\i}a-Vico and H. Seker and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {804,
	title = {Implementation of Data Stream Classification Neural Network Models Over Big Data Platforms},
	booktitle = {International Work-Conference on Artificial Neural Networks (IWANN 2021)},
	year = {2021},
	note = {TIN2015-68454-R, PID2019-107793GB-I00},
	pages = {272{\textendash}280},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	isbn = {978-3-030-85099-9},
	doi = {10.1007/978-3-030-85099-9_22},
	author = {F. Puentes and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {Charte:ClassInformedAEs,
	title = {Reducing Data Complexity using Autoencoders with Class-informed Loss Functions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {In Press},
	year = {2021},
	note = {PID2019-107793GB-I00},
	publisher = {IEEE},
	abstract = {Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separability or boundary shapes, as well as group morphology. Many techniques can transform the data in order to find better features, but few focus on specifically reducing data complexity. Most data transformation methods mainly treat the dimensionality aspect, leaving aside the available information within class labels which can be useful when classes are somehow complex. This paper proposes an autoencoder-based approach to complexity reduction, using class labels in order to inform the loss function about the adequacy of the generated variables. This leads to three different new feature learners, Scorer, Skaler and Slicer. They are based on Fisher{\textquoteright}s discriminant ratio, the Kullback-Leibler divergence and least-squares support vector machines, respectively. They can be applied as a preprocessing stage for a binary classification problem. A thorough experimentation across a collection of 27 datasets and a range of complexity and classification metrics shows that class-informed autoencoders perform better than 4 other popular unsupervised feature extraction techniques, especially when the final objective is using the data for a classification task.},
	issn = {0162-8828},
	doi = {10.1109/TPAMI.2021.3127698},
	author = {David Charte and Francisco Charte and Herrera, Francisco}
}
@conference {Charte:HAIS21,
	title = {Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2021},
	note = {PID2020-119478GB-I00},
	pages = {305{\textendash}315},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Datasets from real-world applications usually deal with many variables and present difficulties when modeling them with traditional classifiers. There is a variety of feature selection and extraction tools that may help with the dimensionality problem, but most of them do not focus on the complexity of the classes. In this paper, a new autoencoder-based model for addressing class complexity in data is introduced, aiming to extract features that present classes in a more separable fashion, thus simplifying the classification task. This is possible thanks to a combination of the standard reconstruction error with a least-squares support vector machine loss function. This model is then applied to a practical use case: classification of chest X-rays according to the presence of COVID-19, showing that learning features that increase linear class separability can boost classification performance. For this purpose, a specific convolutional autoencoder architecture has been designed and trained using the recently published COVIDGR dataset. The proposed model is evaluated by means of several traditional classifiers and metrics, in order to establish the improvements caused by the extracted features. The advantages of using a feature learner and traditional classifiers are also discussed.},
	isbn = {978-3-030-86271-8},
	author = {David Charte and Sevillano-Garc{\'\i}a, Iv{\'a}n and Lucena-Gonz{\'a}lez, Mar{\'\i}a Jes{\'u}s and Mart{\'\i}n-Rodr{\'\i}guez, Jos{\'e} Luis and Francisco Charte and Herrera, Francisco},
	editor = {Sanjurjo Gonz{\'a}lez, Hugo and Pastor L{\'o}pez, Iker and Garc{\'\i}a Bringas, Pablo and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@article {791,
	title = {Synthetic Sample Generation for Label Distribution Learning},
	journal = {Information Sciences},
	volume = {544},
	year = {2021},
	note = {TIN2017-89517-P},
	month = {01/2021},
	pages = {197-213},
	abstract = {Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than a single label or multiple labels. Current LDL methods have proven their effectiveness in many machine learning applications. As of the first formulation of the LDL problem, numerous studies have been carried out that apply the LDL methodology to various real-life problem solving. Others have focused more specifically on the proposal of new algorithms. The purpose of this article is to start addressing the LDL problem as of the data pre-processing stage. The baseline hypothesis is that, due to the high dimensionality of existing LDL data sets, it is very likely that this data will be incomplete and/or that poor data quality will lead to poor performance once applied to the learning algorithms. In this paper, we propose an oversampling method, which creates a superset of the original dataset by creating new instances from existing ones. Then, we apply already existing algorithms to the pre-processed training set in order to validate the effcacy of our method. The effectiveness of the proposed SSG-LDL is verified on several LDL datasets, showing significant improvements to the state-of-the-art LDL methods.},
	keywords = {Data pre-processing, Label Distribution Learning, machine learning, Oversampling},
	doi = {https://doi.org/10.1016/j.ins.2020.07.071},
	author = {Gonzalez, M and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
@article {782,
	title = {A Comprehensive and Didactic Review on Multilabel Learning Software Tools},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {TIN2015-68854-R},
	month = {03/2020},
	pages = {50330-50354},
	abstract = {Machine learning has become an everyday tool in so many fields that there is plenty of software to run many of these algorithms in every device, from supercomputers to embedded appliances. Most of these methods fall into the category known as standard learning, being supervised models (guided by pre-labeled examples) aimed to classify new patterns into exactly one category. This way, machine learning is in charge of getting rid of junk emails, labeling people in a picture, or detecting a fraudulent transaction when using a credit card. Aside from unsupervised learning methods, which are usually applied to group similar patterns, infer association rules and similar tasks, some non-standard supervised machine learning problems have been faced in late years. Among them, multilabel learning is arguably the most popular one. These algorithms aim to produce models in which each data pattern may be linked to several categories at once. Thus, a multilabel classifier generates a set of outputs instead of only one as a standard classifier does. However, software tools for multilabel learning tend to be scarce. This paper provides multilabel researchers with a comprehensive review of the currently available multilabel learning software. It is written following a didactic approach, focusing on how to accomplish each task rather than simply offering a list of programs and websites. The goal is to help finding the most appropriate resource to complete every step, from locating datasets and partitioning them to running many of the multilabel algorithms proposed in the literature until now.},
	keywords = {machine learning, multilabel, non-standard learning, software, Tools},
	doi = {https://doi.org/10.1109/ACCESS.2020.2979787},
	author = {Francisco Charte}
}
@conference {766,
	title = {A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns},
	booktitle = {15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)},
	volume = {1268},
	year = {2020},
	note = {BES-2016-077738},
	pages = {100},
	abstract = {A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining.
The experimental study presented in this paper faces this new proposal regarding an ensemble of one of the most used algorithms within supervised descriptive rule discovery. Results presents a set of patterns with a major interpretability and precision for the new proposal which could be interesting for experts in real-world applications.},
	keywords = {Emerging pattern mining, Fuzzy patterns, Many objective evolutionary algorithm},
	doi = {https://doi.org/10.1007/978-3-030-57802-2_10},
	author = {C. J. Carmona and P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico and M. J. del Jesus}
}
@conference {780,
	title = {A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops },
	booktitle = {15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)},
	year = {2020},
	note = {PID2019-107793GB-I00},
	month = {08/2020},
	pages = {276-285},
	abstract = {Satellite imagery has been consolidated as an accurate option to monitor or classify crops. This is due to the continuous increase in spatial-temporal resolution and the availability of free access to this kind of services. In order to generate crop type maps (a valuable preprocessing step to most remote agriculture monitoring application), time series are built from remote sensing images, and supervised techniques are widely used to classify them. However, one of the main drawbacks of these methods is the lack of labelled data sets to carry out the training process. Unsupervised classification has been less frequently used in this research field. The paper presents an experimental study comparing traditional clustering algorithms (with different dissimilarity measures) for the classification of olive trees and cereal crops from time series remote sensing data. The results obtained provide crucial information for developing novel and more accurate crop mapping algorithms.},
	keywords = {Clustering Crop mappings, Satellite imagery, Time series classification, Unsupervised learning},
	doi = {https://doi.org/10.1007/978-3-030-57802-2_27},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and D. Elizondo and Lipika Deka and M. J. del Jesus}
}
@conference {790,
	title = {Agglomerative Constrained Clustering Through Similarity and Distance Recalculation},
	booktitle = {International Conference on Hybrid Artificial Intelligence Systems},
	year = {2020},
	pages = {424-436},
	abstract = {Constrained clustering has become a topic of considerable interest in machine learning, as it has been shown to produce promising results in domains where only partial information about how to solve the problem is available. Constrained clustering can be viewed as a semi-supervised generalization of clustering, which is traditionally unsupervised. It is able to leverage a new type of information encoded by constraints that guide the clustering process. In particular, this study focuses on instance-level must-link and cannot-link constraints. We propose an agglomerative constrained clustering algorithm, which combines distance-based and clustering-engine adapting methods to incorporate constraints into the partitioning process. It computes a similarity measure on the basis of distances (in the dataset) and constraints (in the constraint set) to later apply an agglomerative clustering method, whose clustering engine has been adapted to consider constraints and raw distances. We prove its capability to produce quality results for the constrained clustering problem by comparing its performance to previous proposals on several datasets with incremental levels of constraint-based information.},
	keywords = {Agglomerative clustering, constrained clustering, Semi-supervised learning, Similarity recalculation},
	doi = {https://doi.org/10.1007/978-3-030-61705-9_35},
	author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Juan Luis Suarez and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
@article {767,
	title = {An analysis of technological frameworks for data streams},
	journal = {Progress in Artificial Intelligence},
	volume = {9},
	year = {2020},
	month = {06/2020},
	pages = {239{\textendash}261},
	abstract = {Real-time data analysis is becoming increasingly important in Big Data environments for addressing data stream issues. To this end, several technological frameworks have been developed, both open-source and proprietary, for the analysis of streaming data. This paper analyzes some open-source technological frameworks available for data streams, detailing their main characteristics. The objective is to facilitate decisions on which framework to use, meeting the needs of data mining methods for data streams. In this sense, there are important factors affecting the choice about which framework is most suitable for this purpose. Some of these factors are the existence of data mining libraries, the available documentation, the maturity of the platform, fault tolerance and processing guarantees, among others. Another decisive factor when choosing a data stream framework is its performance. For this reason, two comparisons have been made: a performance and latency comparison between Spark Streaming, Spark Structured Streaming, Storm, Flink and Samza following the Yahoo Streaming Benchmark methodology, and a comparison between Spark Streaming and Flink with a clustering algorithm for data streaming called streaming K-means.},
	keywords = {Big Data, Big data frameworks, Data stream engines, Data streaming, Technological frameworks},
	doi = {https://doi.org/10.1007/s13748-020-00210-6},
	author = {F. Puentes and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {783,
	title = {An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges},
	journal = {Neurocomputing},
	volume = {404},
	year = {2020},
	note = {TIN2015-68854-R; TIN2017-89517-P; DeepSCOP Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica en Big Data 2018},
	pages = {93-107},
	abstract = {In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.},
	keywords = {Autoencoders, Deep learning, Feature extraction, Representation learning},
	doi = {https://doi.org/10.1016/j.neucom.2020.04.057},
	author = {David Charte and Francisco Charte and M. J. del Jesus and F. Herrera}
}
@article {784,
	title = {Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications},
	journal = {Neurocomputing},
	volume = {410},
	year = {2020},
	note = {TIN2017-85827-P; RTI2018-098913-B-I00; PSI2015-65848-R; PGC2018-098813-B-C31; PGC2018-098813-B-C32; RTI2018-101114-B-I; TIN2017-90135-R; RTI2018-098743-B-I00; RTI2018-094645-B-I00; FPU15/06512; FPU17/04154; FJCI-2017{\textendash}33022; UMA18-FEDERJA-084; ED431C2017/12; ED431G/08; ED431C2018/29; Y2018/EMT-5062; ED431F2018/02; U01 AG024904; W81XWH-12-2-0012},
	pages = {237-270},
	abstract = {Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.},
	keywords = {AI for social well-being, Alzheimer, Artificial intelligence (AI), Artificial neural networks (ANNs), Autism, Big Data, Computational neuroethology, Deep learning, Dyslexia, Emotion recognition, evolutionary computation, Glaucoma, Human{\textendash}machine interaction, machine learning, Neuroscience, Ontologies, Parkinson, Reinforcement learning, Robotics, Virtual reality},
	doi = {https://doi.org/10.1016/j.neucom.2020.05.078},
	author = {Juan M.G{\'o}rriz and Javier Ram{\'\i}rez and Andr{\'e}s Ort{\'\i}z and Francisco J. Mart{\'\i}nez-Murcia and Ferm{\'\i}n Segovia and John Suckling and Matthew Leming and Yu-Dong Zhang and Jos{\'e} Ram{\'o}n {\'A}lvarez-S{\'a}nchez and Guido Bologna and Paula Bonomini and Fernando E. Casado and David Charte and Francisco Charte and Ricardo Contreras and Alfredo Cuesta Infante and Richard J. Duro and Antonio Fern{\'a}ndez Caballero and Eduardo Fern{\'a}ndez Jover and Pedro G{\'o}mez Vilda and Manuel Gra{\~n}a and F. Herrera and Roberto Iglesias and Anna Lekova and Javier de Lope and Ezequiel L{\'o}pez Rubio and Rafael Mart{\'\i}nez Tom{\'a}s and Miguel A. Molina-Cabello and Antonio S. Montemayor and Paulo Novais and Daniel Palacios-Alonso and Juan J. Pantrigo and Bryson R. Payne and F{\'e}lix de la Paz L{\'o}pez and Mar{\'\i}a Ang{\'e}lica Pinninghoff and Mariano Rinc{\'o}n and Jos{\'e} Santos and Karl Thurnhofer-Hemsi and Athanasios Tsanas and Ramiro Varela and Jose M. Ferr{\'a}ndez}
}
@article {774,
	title = {Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines},
	journal = {Information Fusion},
	volume = {54},
	year = {2020},
	note = {TIN2015-68454-R},
	month = {02/2020},
	pages = {44-60},
	abstract = {Classifying data patterns is one of the most recurrent applications in machine learning. The number of input features influences the predictive performance of many classification models. Most classifiers work with high-dimensional spaces. Therefore, there is a great interest in facing the task of reducing the input space. Manifold learning has been shown to perform better than classical dimensionality reduction approaches, such as Principal Component Analysis and Linear Discriminant Analysis. In this sense, Autoencoders (AEs) provide an automated way of performing feature fusion, finding the best manifold to reconstruct the data. There are several models and architectures of AEs. For this reason, in this study an exhaustive analysis of the predictive performance of different AEs models with a large number of datasets is proposed, aiming to provide a set of useful guidelines. These will allow users to choose the appropriate AE model for each case, depending on data traits and the classifier to be used. A thorough empirical analysis is conducted including four AE models, four classification paradigms and a group of datasets with a variety of traits. A convenient set of rules to follow is obtained as a result.},
	keywords = {Autoencoders, classification, Deep learning, Dimensionality reduction, Feature fusion},
	doi = {https://doi.org/10.1016/j.inffus.2019.07.004},
	author = {Pulgar, Francisco J. and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@article {789,
	title = {DILS: Constrained clustering through dual iterative local search},
	journal = {Computers \& Operations Research},
	volume = {121},
	year = {2020},
	note = {TIN2017- 89517-P;  PP2016.PRI.I.02.},
	pages = {104979},
	abstract = {Clustering has always been a powerful tool in knowledge discovery. Traditionally unsupervised, it has received renewed attention recently as it has shown to produce better results when provided with new types of information, thus leading to a new kind of semi-supervised learning: constrained clustering. This technique is a generalization of traditional clustering that considers additional information encoded by constraints. Constraints can be given in the form of instance-level must-link and cannot-link constraints, which is the focus of this paper. We propose a new metaheuristic algorithm, the Dual Iterative Local Search, and prove its ability to produce quality results for the constrained clustering problem. We compare the results obtained by this proposal to those obtained by the state-of-the-art algorithms on 25 datasets with incremental levels of constraint-based information, supporting our conclusions with the aid of Bayesian statistical tests.},
	keywords = {Cannot-link, constrained clustering, Dual iterative local search, Instance-level, Must-link},
	doi = {https://doi.org/10.1016/j.cor.2020.104979},
	author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
@article {765,
	title = {E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments},
	journal = {Neurocomputing},
	volume = {415},
	year = {2020},
	note = {TIN2015-68454-R; BES-2016-07773},
	month = {11/2020},
	pages = {60-73},
	abstract = {In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process.

The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives.},
	keywords = {Emerging pattern mining Evolutionary fuzzy systems Multi-objective evolutionary algorithm Big data},
	doi = {https://doi.org/10.1016/j.neucom.2020.07.007},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and D. Elizondo and C. J. Carmona}
}
@article {779,
	title = {El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodolog{\'\i}as y herramientas},
	journal = {Ense{\~n}anza y Aprendizaje de Ingenier{\'\i}a de Computadores},
	number = {10},
	year = {2020},
	abstract = {La transici{\'o}n de una modalidad de ense{\~n}anza tradicional y presencial a una de tipo remoto, provocada por el confinamiento a ra{\'\i}z del coronavirus SARS-CoV-2, ha implicado cambios que han debido realizarse de manera acelerada y que afectan no solo al desarrollo de las clases, sino tambi{\'e}n a las actividades pr{\'a}cticas en laboratorio, de comunicaci{\'o}n y de evaluaci{\'o}n de las competencias de los estudiantes. En este trabajo exponemos c{\'o}mo hemos afrontado dicha transici{\'o}n en asignaturas del {\'a}rea Arquitectura y tecnolog{\'\i}a de computadores en la Universidad de Ja{\'e}n, as{\'\i} como la planificaci{\'o}n que hemos llevado a cabo para el pr{\'o}ximo curso 2020/2021 ante el nivel de incertidumbre sobre c{\'o}mo se desarrollar{\'a}.},
	keywords = {Aprendizaje activo, Aprendizaje basado en proyectos, Metodolog{\'\i}as de aprendizaje, Modelos de evaluaci{\'o}n},
	doi = {http://dx.doi.org/10.30827/Digibug.64779},
	author = {Francisco Charte and A.J. Rivera-Rivas and Medina, J. and Espinilla, Macarena}
}
@article {773,
	title = {EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search},
	journal = {Integrated Computer-Aided Engineering},
	volume = {27},
	number = {3},
	year = {2020},
	month = {05/2020},
	pages = {211-231},
	abstract = {Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time. },
	keywords = {Autoencoder, evolutionary methods, Representation learning},
	doi = {https://doi.org/10.3233/ICA-200619},
	author = {Francisco Charte and A.J. Rivera-Rivas and Francisco Mart{\'\i}nez and M. J. del Jesus}
}
@article {764,
	title = {FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {28},
	year = {2020},
	note = {BES-2016-077738},
	month = {12/2020},
	pages = { 3193-3203},
	abstract = {Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorized as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns (EPs) are interesting in a data stream context as easy, fast, and reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory, and continuous model updates. In this article, an approach for the extraction of EPs in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential.},
	keywords = {Data stream mining, emerging pattern mining (EPM), evolutionary  fuzzy  systems  (EFSs), multiobjective evolutionary algorithms (EAs)},
	doi = {10.1109/TFUZZ.2020.2992849},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and H. Seker and M. J. del Jesus}
}
@conference {788,
	title = {Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism},
	booktitle = {GECCO {\textquoteright}20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
	year = {2020},
	note = {TIN2017-89517-P; PP2016.PRI.I.02.},
	month = {06/2020},
	pages = {333{\textendash}341},
	abstract = {Clustering has always been a topic of interest in knowledge discovery, it is able to provide us with valuable information within the unsupervised machine learning framework. It received renewed attention when it was shown to produce better results in environments where partial information about how to solve the problem is available, thus leading to a new machine learning paradigm: semi-supervised machine learning. This new type of information can be given in the form of constraints, which guide the clustering process towards quality solutions. In particular, this study considers the pairwise instance-level must-link and cannot-link constraints. Given the ill-posed nature of the constrained clustering problem, we approach it from the multiobjective optimization point of view. Our proposal consists in a memetic elitist evolutionary strategy that favors exploitation by applying a local search procedure to the elite of the population and transferring its results only to the external population, which will also be used to generate new individuals. We show the capability of this method to produce quality results for the constrained clustering problem when considering incremental levels of constraint-based information. For the comparison with state-of-the-art methods, we include previous multi-objective approaches, single-objective genetic algorithms and classic constrained clustering methods.},
	keywords = {constrained clustering, memetic elitis MOEA, multiobjective optimization, pairwise instance- level constraints, Semi-supervised learning},
	doi = {https://doi.org/10.1145/3377930.3390187},
	author = {Germ{\'a}n Gonz{\'a}lez-Almagro and Rosales-P{\'e}rez, Alejandro and Luengo, Juli{\'a}n and J. R. Cano and Garc{\'\i}a, Salvador}
}
@article {786,
	title = {ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning},
	journal = {Applied Sciences},
	volume = {10},
	year = {2020},
	note = {TIN2017-89517-P},
	pages = {3089},
	abstract = {Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets.},
	doi = {https://doi.org/10.3390/app10093089},
	author = {Gonzalez, M and J. R. Cano and Garc{\'\i}a, Salvador}
}
@article {787,
	title = {Similarity-based and Iterative Label Noise Filters for Monotonic Classification},
	journal = {Proceedings of the 53rd Hawaii International Conference on System Sciences},
	year = {2020},
	note = {TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS - Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica 2016},
	pages = {1698-1706},
	abstract = {Monotonic ordinal classification has received an increasing interest in the latest years. Building monotone models from these problems usually requires datasets that verify monotonic relationships among the samples. When the monotonic relationships are not met, changing the labels may be a viable option, but the risk is high: wrong label changes would completely change the information contained in the data. In this work, we tackle the construction of monotone datasets by removing the wrong or noisy examples that violate monotonicity restrictions. We propose two monotonic noise filtering algorithms to preprocess the ordinal datasets and improve the monotonic relations between instances. The experiments are carried out over eleven ordinal datasets, showing that the application of the proposed filters improve the prediction capabilities over different levels of noise.},
	keywords = {Monotonic classification, noise, noise filter, Ordinal classification, Soft Computing: Theory Innovations and Problem Solving Benefits},
	doi = {https://doi.org/10.24251/HICSS.2020.210},
	author = {J. R. Cano and Luengo, Juli{\'a}n and Garc{\'\i}a, Salvador}
}
@article {795,
	title = {Temporal association rule mining: An overview considering the time variable as an integral or implied component},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {10},
	year = {2020},
	note = {TIN2017-89517-P; P18-RT-2248},
	month = {04/2020},
	abstract = {Association rules are commonly used to provide decision-makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real-life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well-defined framework that allows researchers both to easily locate the previous proposals and to propose well-grounded methods in the future. To accomplish both objectives, a two-level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed.

This article is categorized under:

    Technologies > Association Rules
    Algorithmic Development > Association Rules
},
	keywords = {calendar association rules, cyclic association rules, data mining, sequential association rules, temporal association rules},
	doi = {https://www.doi.org/10.1002/widm.1367},
	author = {Alberto Segura-Delgado and M. J. Gacto and Alcal{\'a}, Rafael and J. Alcal{\'a}-Fdez}
}
@article {272,
	title = {A Big Data Approach for the Extraction of Fuzzy Emerging Patterns},
	journal = {Cognitive Computation},
	volume = {11},
	year = {2019},
	note = {TIN2015-68454-R; BES-2016-077738 },
	month = {01/2019},
	pages = {400{\textendash}417},
	doi = {10.1007/s12559-018-9612-7},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@conference {772,
	title = {A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy},
	booktitle = {International Work-Conference on Artificial Neural Networks},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {270-280},
	abstract = {A convenient data preprocessing has proven to be crucial in order to achieve high levels of accuracy, time series being no exception. For this kind of forecasting tasks, several specialized preprocessing methods have been described, being trend analysis and seasonal analysis some of them. Several have been formally grouped around a methodology that is always applied to state of the art time series forecasting models, like the well known ARIMA.

LSTM is a relatively novel architecture which has been specifically designed to get rid of the vanishing gradient problem. In these models, great results have been seen when applied for time series forecasting. Still, little is known about the impact of these traditional preprocessing methods on the accuracy of LSTM.

In this work an empirical analysis on how classical time series preprocessing methods influence LSTM results is conducted. That all considered ones can potentially improve LSTM performance is concluded, being the seasonal component removal the filter that achieves better, most robust accuracy gain.},
	keywords = {LSTM, Preprocessing, time series},
	doi = {https://doi.org/10.1007/978-3-030-20521-8_23},
	author = {Daniel Trujillo Viedma and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@conference {770,
	title = {A Showcase of the Use of Autoencoders in Feature Learning Applications},
	booktitle = {International Work-Conference on the Interplay Between Natural and Artificial Computation},
	year = {2019},
	note = {TIN2015-68854-R; TIN2017-89517-P},
	month = {05/2019},
	pages = {412-421},
	abstract = {Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing.

This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, ruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.},
	keywords = {Autoencoders, Deep learning, Feature learning},
	doi = {https://doi.org/10.1007/978-3-030-19651-6_40},
	author = {David Charte and Francisco Charte and M. J. del Jesus and F. Herrera}
}
@article {297,
	title = {A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations},
	journal = {Progress in Artificial Intelligence},
	volume = {8},
	year = {2019},
	note = {TIN2017-89517-P; TIN2015-68454-R},
	month = {11},
	pages = {1-14},
	abstract = {Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.},
	issn = {2192-6360},
	doi = {10.1007/s13748-018-00167-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and F. Herrera}
}
@conference {776,
	title = {Automatic Time Series Forecasting with GRNN: A Comparison with Other Models},
	booktitle = {International Work-Conference on Artificial Neural Networks},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {198-209},
	abstract = {In this paper a methodology based on general regression neural networks for forecasting time series in an automatic way is presented. The methodology is aimed at achieving an efficient and fast tool so that a large amount of time series can be automatically predicted. In this sense, general regression neural networks present some interesting features, they have a fast single-pass learning and produce deterministic results. The methodology has been implemented in the R environment. A study of packages in R for automatic time series forecasting, including well-known statistical and computational intelligence models such as exponential smoothing, ARIMA or multilayer perceptron, is also done, together with an experimentation on running time and forecast accuracy based on data from the NN3 forecasting competition.},
	keywords = {Automatic forecasting, General regression neural networks, time series forecasting},
	doi = {https://doi.org/10.1007/978-3-030-20521-8_17},
	author = {Francisco Mart{\'\i}nez and Francisco Charte and A.J. Rivera-Rivas and Fr{\'\i}as, Mar{\'\i}a Pilar}
}
@conference {771,
	title = {Automating Autoencoder Architecture Configuration: An Evolutionary Approach},
	booktitle = {International Work-Conference on the Interplay Between Natural and Artificial Computation},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {339-349},
	abstract = {Learning from existing data allows building models able to classify patterns, infer association rules, predict future values in time series and much more. Choosing the right features is a vital step of the learning process, specially while dealing with high-dimensional spaces. Autoencoders (AEs) have shown ability to conduct manifold learning, compressing the original feature space without losing useful information. However, there is no optimal AE architecture for all datasets. In this paper we show how to use evolutionary approaches to automate AE architecture configuration. First, a coding to embed the AE configuration in a chromosome is proposed. Then, two evolutionary alternatives are compared against exhaustive search. The results show the great superiority of the evolutionary way.},
	keywords = {Autoencoder, Deep learning, Evolutionary, Optimization},
	doi = {https://doi.org/10.1007/978-3-030-19591-5_35},
	author = {Francisco Charte and A.J. Rivera-Rivas and Francisco Mart{\'\i}nez and M. J. del Jesus}
}
@article {292,
	title = {Dealing with difficult minority labels in imbalanced mutilabel data sets},
	journal = {Neurocomputing},
	volume = {326},
	year = {2019},
	note = {TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765},
	pages = {39{\textendash}53},
	abstract = {Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In addition, this characteristic challenges many of the existent preprocessing algorithms. Furthermore, the concurrence between imbalanced labels can make harder the learning from certain labels. These are what we call difficult labels. In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed. Specific metrics to assess this trait in multilabel datasets, called SCUMBLE (Score of ConcUrrence among iMBalanced LabEls) and SCUMBLELbl, are presented along with REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels), a new algorithm aimed to relax label concurrence. How to deal with this problem using the R mldr package is also outlined.},
	doi = {10.1016/j.neucom.2016.08.158},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@book {304,
	title = {El pasado de la computaci{\'o}n personal. Historia de la microinform{\'a}tica (2a Edici{\'o}n)},
	year = {2019},
	publisher = {Editorial Universidad de Ja{\'e}n},
	organization = {Editorial Universidad de Ja{\'e}n},
	abstract = {By the end of the 2010s computers have become ubiquitous. Users are not even aware that they carry them: in their cellphones, smart watches, and other similar devices. This evolution has been made possible as a result of the revolution that took place in the 1970s and the 1980s. It was then, with the emergence of microprocessors and microcomputers, that computing began to arrive at people{\textquoteright}s houses. This book recovers the history of those devices.},
	isbn = {978-84-9159-186-3},
	author = {Francisco Charte and Lina Garc{\'\i}a}
}
@article {752,
	title = {Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ?},
	journal = {IEEE Computational Intelligence},
	volume = {1},
	number = {14},
	year = {2019},
	note = {TIN2015-68454-R; TIN2015-67661-P; TIN2017-89517-P},
	pages = {69-81},
	abstract = {Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data.},
	issn = {1556-603X},
	doi = {10.1109/TFUZZ.2018.2814577},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and O. Cord{\'o}n and F. Marcelloni and F. Herrera}
}
@article {794,
	title = {Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	month = {08/2019},
	pages = { 108916-108939},
	abstract = {In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, Regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component Regression, Multivariate Adaptive Regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.},
	keywords = {data mining, experimental study., regression algorithms, supervised learning},
	doi = {https://doi.org/10.1109/ACCESS.2019.2933261},
	author = {M. J. Gacto and Jose Manuel Soto-Hidalgo and J. Alcal{\'a}-Fdez and Alcal{\'a}, Rafael}
}
@article {CANO2019,
	title = {Label noise filtering techniques to improve monotonic classification},
	journal = {Neurocomputing},
	volume = {353},
	year = {2019},
	note = {TIN2014-57251-P; TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS},
	month = {08/2019},
	pages = {83-95},
	abstract = {The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are very frequent. To construct predictive monotone models from those problems, many classifiers require as input a data set satisfying the monotonicity relationships among all samples. Changing the class labels of the data set (relabeling) is useful for this. Relabeling is assumed to be an important building block for the construction of monotone classifiers and it is proved that it can improve the predictive performance. In this paper, we will address the construction of monotone datasets considering as noise the cases that do not meet the monotonicity restrictions. For the first time in the specialized literature, we propose the use of noise filtering algorithms in a preprocessing stage with a double goal: to increase both the monotonicity index of the models and the accuracy of the predictions for different monotonic classifiers. The experiments are performed over 12 datasets coming from classification and regression problems and show that our scheme improves the prediction capabilities of the monotonic classifiers instead of being applied to original and relabeled datasets. In addition, we have included the analysis of noise filtering process in the particular case of wine quality classification to understand its effect in the predictive models generated.},
	keywords = {Monotonic classification, Noise filtering, Ordinal classification, Preprocessing},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2018.05.131},
	url = {http://www.sciencedirect.com/science/article/pii/S092523121930325X},
	author = {J. R. Cano and J. Luengo and S. Garc{\'\i}a}
}
@article {785,
	title = {Monotonic classification: An overview on algorithms, performance measures and data sets},
	journal = {Neurocomputing},
	volume = {341},
	year = {2019},
	note = {TIN2017-89517-P; TIN2015-70308-REDT; TIN2014-54583-C2-1-R; TEC2015-69496-R},
	month = {05/2019},
	pages = {168-182},
	abstract = {Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.},
	keywords = {Monotonic classification, Monotonic data sets, Ordinal classification, Software Performance metrics, Taxonomy},
	doi = {https://doi.org/10.1016/j.neucom.2019.02.024},
	author = {J. R. Cano and Pedro Antonio Guti{\'e}rrez and Bartosz Krawczyk and Michat Wo{\'z}niak and Garc{\'\i}a, Salvador}
}
@article {777,
	title = {predtoolsTS: R package for streamlining time series forecasting},
	journal = {Progress in Artificial Intelligence},
	volume = {8},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {06/2019},
	pages = {505{\textendash}510},
	abstract = {Time series forecasting is a field of interest in many areas. Classically, statistical methods have been used to address this problem. In recent years, machine learning (ML) algorithms have been also applied with satisfactory results. However, ML software packages are not skilled to deal with raw sequences of temporal data, and therefore, it is necessary to transform these time series. This paper presents predtoolsTS, an R package that provides a uniform interface for applying both statistical and ML methods to time series forecasting. predtoolsTS comprises four modules: preprocessing, modeling, prediction and postprocessing, in order to deal with the whole process of time series forecasting.},
	keywords = {machine learning, R, time series forecasting},
	doi = {https://doi.org/10.1007/s13748-019-00193-z},
	author = {Francisco Charte and Alberto Vico and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@article {293,
	title = {REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization},
	journal = {Neurocomputing},
	volume = {326},
	year = {2019},
	note = {TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765},
	pages = {110{\textendash}122},
	abstract = {The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification. A handful of multilabel resampling methods have been proposed in late years, aiming to balance the labels distribution. However, these methods have to face a new obstacle, specific for multilabel data, as is the joint appearance of minority and majority labels in the same data patterns. We presented recently a new algorithm designed to decouple imbalanced labels concurring in the same instance, called REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels). The goal of this work is to propose REMEDIAL-HwR (REMEDIAL Hybridization with Resampling), a procedure to hybridize this method with some of the best resampling algorithms available in the literature, including random oversampling, heuristic undersampling and synthetic sample generation techniques. These hybrid methods are then empirically analyzed, determining how their behavior is influenced by the label decoupling process. The analysis of results shows that the proposed method improves certain classifiers performance when it is applied over imbalanced datasets with label concurrence. In addition, a noteworthy set of guidelines on the combined use of these techniques can be drawn from the conducted experimentation.},
	doi = {10.1016/j.neucom.2017.01.118},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {295,
	title = {Ruta: implementations of neural autoencoders in R},
	journal = {Knowledge-Based Systems},
	volume = {174},
	year = {2019},
	note = {TIN2015-68854-R,BigDaP-TOOLS},
	month = {06/2019},
	pages = {4-8},
	abstract = {Autoencoders are neural networks which perform feature learning on data. Many variants can be found in the literature, but their implementations are scarce, in separate software pieces and utilizing different languages and frameworks. The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to the main functionalities as well as full customization of their diverse aspects.},
	doi = {-},
	author = {David Charte and F. Herrera and Francisco Charte}
}
@article {796,
	title = {Smartdata: Data preprocessing to achieve smart data in R},
	journal = {Neurocomputing},
	volume = {360},
	year = {2019},
	note = {BigDaP-TOOLS - Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica 2016},
	month = {09/2019},
	pages = {1-13},
	abstract = {As the amount of data available exponentially grows, data scientists are aware that finding the value in the data is key to a successful data exploiting. However, the data rarely presents itself in a ordered, clean way. In opposition to dealing with raw data, the term smart data is becoming more and more visible both in the specialized literature and companies. While software packages publicly exist to deal with raw data, there is no unified framework that encompasses all the required fields to transform such raw data to smart data. In this paper, the novel smartdata package is introduced. Written in R and available at CRAN repository, it includes the most recent and relevant algorithms to treat raw data from multiple perspectives, now unified under a simple yet powerful API, which enables the data scientist to easily pipeline their application. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript.},
	keywords = {Data preprocessing, machine learning, Preprocessing, Smart data},
	doi = {https://doi.org/10.1016/j.neucom.2019.06.006},
	author = {I. Cordon and Luengo, Juli{\'a}n and Garc{\'\i}a, Salvador and F. Herrera and Francisco Charte}
}
@article {278,
	title = {Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments},
	journal = {Big Data Analytics},
	volume = {4},
	number = {1},
	year = {2019},
	note = {TIN2015-68454-R; BES-2016-077738},
	pages = {1},
	issn = {2058-6345},
	doi = {10.1186/s41044-018-0038-8},
	url = {https://doi.org/10.1186/s41044-018-0038-8},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {763,
	title = {Subgroup Discovery on Multiple Instance Data},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {12},
	year = {2019},
	note = {TIN2017-83445-P; TIN2015-68454-R},
	month = {12/2019},
	pages = {1602-1612},
	abstract = {To date, the subgroup discovery (SD) task has been considered in problems where a target variable is unequivocally described by a set of features, also known as instance. Nowadays, however, with the increasing interest in data storage, new data structures are being provided such as the multiple instance data in which a target variable value is ambiguously defined by a set of instances. Most of the proposals related to multiple instance data are based on predictive tasks and no supervised descriptive analysis can be provided when data is organized in this way. At this point, the aim of this work is to extend the SD task to cope with this type of data. SD is a really interesting task that aims at discovering interesting relationships between different features with respect to a specific target variable that is of interest for the user or the problem under study. In this regard, this paper presents three different approaches for mining interesting subgroups in multiple instance problems. The proposed models represent three different ways of tackling the problem and they are based on three well-known algorithms in the SD field: SD-Map (exhaustive search approach), CGBA-SD (Comprehensible Grammar-Based Algorithm for Subgroup Discovery) and NMEEF-SD (multi-objective evolutionary fuzzy system). The proposals have been tested on a wide set of datasets, including 10 real-world and 20 synthetic datasets, aiming at describing how the three methodologies behave on different scenarios. Any comparison is unfair since they are completely different methodologies.},
	keywords = {Metaheuristics, Multi-instance data, Subgroup discovery, Supervised descriptive patterns},
	doi = {https://doi.org/10.2991/ijcis.d.191213.001},
	author = {J. M. Luna and C. J. Carmona and A.M. Garc{\'\i}a-Vico and M. J. del Jesus and S. Ventura}
}
@article {778,
	title = {Time Series Forecasting with KNN in R: the tsfknn Package},
	journal = {The R Journal},
	volume = {11},
	number = {2},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {12/2019},
	pages = {229-242},
	abstract = {In this paper the tsfknn package for time series forecasting using k-nearest neighbor regression is described. This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies and among different functions to aggregate the targets of the nearest neighbors. It is also possible to assess the forecast accuracy of the KNN model.},
	doi = {https://doi.org/10.32614/RJ-2019-004},
	author = {Francisco Mart{\'\i}nez and Fr{\'\i}as, Mar{\'\i}a Pilar and Francisco Charte and A.J. Rivera-Rivas}
}
@conference {307,
	title = {A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders},
	booktitle = {19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018},
	year = {2018},
	note = {TIN2015-68854-R; FPU16/00324},
	month = {11},
	pages = {439{\textendash}447},
	address = {Madrid (Spain)},
	abstract = {The problem of high dimensionality is a challenge when facing machine learning tasks. A high dimensional space has a negative effect on the predictive performance of many methods, specifically, classification algorithms. There are different proposals that arise to mitigate the effects of this phenomenon. In this sense, models based on deep learning have emerged.},
	isbn = {978-3-030-03493-1},
	doi = {10.1007/978-3-030-03493-1_46},
	author = {F. Pulgar-Rubio and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {10.1007/978-3-319-92639-1_23,
	title = {A First Attempt on Monotonic Training Set Selection},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2018},
	pages = {277{\textendash}288},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Monotonicity constraints frequently appear in real-life problems. Many of the monotonic classifiers used in these cases require that the input data satisfy the monotonicity restrictions. This contribution proposes the use of training set selection to choose the most representative instances which improves the monotonic classifiers performance, fulfilling the monotonic constraints. We have developed an experiment on 30 data sets in order to demonstrate the benefits of our proposal.},
	isbn = {978-3-319-92639-1},
	author = {J. R. Cano and S. Garc{\'\i}a},
	editor = {de Cos Juez, Francisco Javier and Villar, Jos{\'e} Ram{\'o}n and de la Cal, Enrique A. and Herrero, {\'A}lvaro and Quinti{\'a}n, H{\'e}ctor and S{\'a}ez, Jos{\'e} Ant{\'o}nio and Corchado, Emilio}
}
@conference {277,
	title = {A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R, TIN2017-89517-P},
	pages = {1316-1317},
	author = {A. Fern{\'a}ndez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {333,
	title = {A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {-},
	month = {10},
	pages = {949{\textendash}950},
	address = {Granada (Spain)},
	abstract = {This is a summary of our article published in Information Fusion to be part of the CAEPIA-18 KeyWorks.},
	isbn = {978-88-61970-00-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@article {Charte2018,
	title = {A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations},
	journal = {Progress in Artificial Intelligence},
	year = {2018},
	month = {Nov},
	abstract = {Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.},
	issn = {2192-6360},
	doi = {10.1007/s13748-018-00167-7},
	url = {https://doi.org/10.1007/s13748-018-00167-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and F. Herrera}
}
@article {266,
	title = {A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy},
	journal = {Knowledge-Based Systems},
	volume = {139},
	year = {2018},
	note = {TIN2014-915 57251-P, TIN2015-68454-R},
	pages = {89-100},
	doi = {10.1016/j.knosys.2017.10.015},
	author = {C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@article {386,
	title = {AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {12},
	year = {2018},
	note = {TIN2015-68854-R; FPU16/00324},
	month = {11/2018},
	pages = {436-452},
	abstract = {High dimensionality tends to be a challenge for most machine learning tasks, including classification. There are different classification methodologies, of which instance-based learning is one. One of the best known members of this family is the k-nearest neighbors (kNNs) algorithm. Its strategy relies on searching a set of nearest instances. In high-dimensional spaces, the distances between examples lose significance. Therefore, kNN, in the same way as many other classifiers, tends to worsen its performance as the number of input variables grows. In this study, AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented. Aiming to obtain a new representation of the data, having a lower dimensionality but with more informational features, AEkNN internally uses autoencoders. From this new vector of features the computed distances should be more significant, thus providing a way to choose better neighbors. An experimental evaluation of the new proposal is conducted, analyzing several configurations and comparing them against the original kNN algorithm and classical dimensionality reduction methods. The obtained conclusions demonstrate that AEkNN offers better results in predictive and runtime performance.},
	issn = {1875-6883},
	doi = {10.2991/ijcis.2019.0025},
	url = {https://www.atlantis-press.com/article/125905686},
	author = {F. Pulgar-Rubio and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {306,
	title = {An Approximation to Deep Learning Touristic-Related Time Series Forecasting},
	booktitle = {19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018},
	year = {2018},
	note = {TIN2015-68854-R},
	month = {11},
	pages = {448{\textendash}456},
	address = {Madrid (Spain)},
	abstract = {Tourism is one of the biggest economic activities around the world. This means that an adequate planning of existing resources becomes crucial. Precise demand-related forecasting greatly improves this planning. Deep Learning models are showing an greatly improvement on time-series forecasting, particularly the LSTM, which is designed for this kind of tasks. This article introduces the touristic time-series forecasting using LSTM, and compares its accuracy against well known models RandomForest and ARIMA. Our results shows that new LSTM models achieve the best accuracy.},
	isbn = {978-3-030-03493-1},
	doi = {10.1007/978-3-030-03493-1_47},
	author = {Daniel Trujillo and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@article {261,
	title = {An Overview of Emerging Pattern Mining in Supervised Descriptive Rule Discovery: Taxonomy, Empirical Study, Trends and Prospects},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {8},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	doi = {10.1002/widm.1231},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and D. Mart{\'\i}n and M. Garc{\'\i}a-Borroto and M. J. del Jesus}
}
@conference {331,
	title = {An{\'a}lisis del impacto de datos desbalanceados en el rendimiento predictivo de redes neuronales convolucionales},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {TIN2015-68454-R; FPU16/00324},
	month = {10},
	pages = {1213{\textendash}1218},
	address = {Granada (Spain)},
	abstract = {En los {\'u}ltimos a{\~n}os han surgido nuevas propuestas basadas en Deep Learning para afrontar la tarea de clasificaci{\'o}n. Estas propuestas han obtenido buenos resultados en algunos campos, por ejemplo, en reconocimiento de im{\'a}genes. Sin embargo, existen factores que deben ser analizados para valorar su influencia en los resultados obtenidos con estos nuevos modelos. En este trabajo se analiza la clasificaci{\'o}n de datos desbalanceados con redes neuronales convolucionales (Convolutional Neural Networks-CNNs). Para hacerlo, se han llevado a cabo una serie de tests donde se reconocen im{\'a}genes mediante CNNs. As{\'\i}mismo, se utilizan conjuntos de datos con diferente grado de desbalanceo. Los resultados demuestran que el desequilibrio afecta negativamente al rendimiento predictivo.},
	isbn = {978-88-61970-00-7},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and Francisco Charte and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@conference {759,
	title = {An{\'a}lisis preliminar de marcos tecnol{\'o}gicos en data stream},
	booktitle = {II Workshop en Big Data y an{\'a}lisis de datos escalable},
	year = {2018},
	month = {10},
	pages = {1117-1122},
	address = {Granada (Espa{\~n}a)},
	author = {F. Puentes and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {276,
	title = {Atipicidad: Medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2014-916 57251-P, TIN2015-68454-R},
	pages = {827-828},
	author = {C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/978-3-319-91479-4_61,
	title = {Credal C4.5 with Refinement of~Parameters},
	booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications},
	year = {2018},
	pages = {739{\textendash}747},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Recently, a classification method called Credal C4.5 (CC4.5) has been presented which combines imprecise probabilities and the C4.5 algorithm. The action of the CC4.5 algorithm depends on a parameter s. In previous works, it has been shown that this parameter has relation with the degree of overfitting of the model. The noise level of a data set can influence on the choice of a good value for s. In this paper, it is presented a new method based on the CC4.5 method with a refining of its parameter in the time of training. The new method has an equivalent performance than CC4.5 with the best value of s for each level noise.},
	isbn = {978-3-319-91479-4},
	author = {Mantas, Carlos J. and Abell{\'a}n, Joaqu{\'\i}n and Castellano, Javier G. and J. R. Cano and Moral, Seraf{\'\i}n},
	editor = {Medina, Jes{\'u}s and Ojeda-Aciego, Manuel and Verdegay, Jos{\'e} Luis and Perfilieva, Irina and Bouchon-Meunier, Bernadette and Yager, Ronald R.}
}
@article {MARTINEZ201838,
	title = {Dealing with seasonality by narrowing the training set in time series forecasting with kNN},
	journal = {Expert Systems with Applications},
	volume = {103},
	year = {2018},
	pages = {38 - 48},
	abstract = {In this paper, a new strategy for dealing with time series exhibiting a seasonal pattern is proposed. The strategy is applied in the context of time series forecasting using kNN regression. The key idea is to forecast every different season using a different specialized kNN learner. Each learner is specialized because its training set only contains examples whose targets belong to the season that is able to forecast. This way, the forecast of a specialized kNN learner is an aggregation of target values of the same season, reducing the likelihood of misleading forecasts. Although the strategy is applied to kNN, we think that other computational intelligence approaches could take advantage of it.},
	keywords = {NN regression, Seasonal time series, time series forecasting},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2018.03.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417418301441},
	author = {Francisco Mart{\'\i}nez and Mar{\'\i}a Pilar Fr{\'\i}as and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@article {270,
	title = {Evolutionary Data Mining and Applications: A Revision on the Most Cited Papers from the Last 10 Years (2007-2017)},
	journal = {WIREs Data Mining and Knowledge Discover},
	volume = {8},
	year = {2018},
	note = {TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765},
	pages = {1-17},
	doi = {https://doi.org/10.1002/widm.1239},
	author = {R. Alcal{\'a} and M. J. Gacto and J. Alcal{\'a}-Fdez}
}
@article {628,
	title = {Improvement of subgroup descriptions in noisy data by detecting exceptions},
	journal = {Progress in Artificial Intelligence},
	volume = {7},
	year = {2018},
	note = {TIN2015-68454-R},
	pages = {55-64},
	doi = {10.1007/s13748-017-0131-7},
	author = {P. Gonz{\'a}lez and A.M. Garc{\'\i}a-Vico and C. J. Carmona and M. J. del Jesus}
}
@article {8049471,
	title = {Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming},
	journal = {IEEE Transactions on Cybernetics},
	volume = {48},
	number = {11},
	year = {2018},
	month = {Nov},
	pages = {3030-3044},
	abstract = {Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.},
	keywords = {Association rules, Computer science, context awareness, context-aware association rules mining, context-sensitive factors, contextual features, contextual information, contextual-sensitive features, data mining, Feature extraction, genetic algorithms, Genetic programming, Grammar, grammar-based genetic programming methodology, grammars, pattern mining process, Proposals, ubiquitous computing},
	issn = {2168-2267},
	doi = {10.1109/TCYB.2017.2750919},
	author = {J. M. Luna and M. Pechenizkiy and M. J. del Jesus and S. Ventura}
}
@conference {275,
	title = {Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de grandes vol{\'u}menes de datos y flujos continuos de datos},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {1402-1407},
	author = {A.M. Garc{\'\i}a-Vico}
}
@article {271,
	title = {MOEA-EFEP: Multi-Objective Evolutionary Algorithm for Extracting Fuzzy Emerging Patterns},
	journal = {IEEE Transaction on Fuzzy Systems},
	volume = {26},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {2861-2872},
	doi = {10.1109/TFUZZ.2018.2814577},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {273,
	title = {MOEA-EFEP: Un algoritmo evolutivo multi-objetivo para la extracción de patrones emergentes difusos},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {671-672},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {298,
	title = {Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {8},
	year = {2018},
	note = {-},
	pages = {67{\textendash}83},
	abstract = {The design and manufacture of hardware is expensive, both in time and in economic investment, which is why integrated circuits are always manufactured in large volume, to take advantage of economies of scale. For this reason, the majority of processors manufactured are general purpose, thus expanding its range of applications. In recent years, however, more and more processors are being manufactured for specific applications, including those aimed at accelerating work with deep neural networks. This article introduces the need for this type of specialized hardware, describing its purpose, operation and current implementations.},
	issn = {2173-8688},
	author = {A.J. Rivera-Rivas and Francisco Charte and Espinilla, Macarena and M.D. P{\'e}rez-Godoy}
}
@conference {760,
	title = {Reglas de asociaci{\'o}n en datos multi-instancia mediante programaci{\'o}n gen{\'e}tica gramatical},
	booktitle = {IX Simposio de Teor{\'\i}a y Aplicaciones de la Miner{\'\i}a de Datos},
	year = {2018},
	month = {10},
	pages = {815-820},
	address = {Granada},
	author = {J.M. Luna and O. Reyes and M. J. del Jesus and S. Ventura}
}
@article {291,
	title = {Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository},
	journal = {Neurocomputing},
	volume = {289},
	year = {2018},
	note = {TIN2014-57251-P,TIN2015-68454-R,BigDaP-TOOLS},
	pages = {68{\textendash}85},
	abstract = {New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, training, analysis of results and, finally, comparison with existing methods. This last step is often hampered since it involves using exactly the same datasets, partitioned in the same way and using the same validation strategy. In this paper we present a set of tools whose objective is to facilitate the management of multi-label datasets, aiming to standardize the experimentation procedure. The two main tools are an R package, mldr.datasets, and a web repository with datasets, Cometa. Together, these tools will simplify the collection of datasets, their partitioning, documentation and export to multiple formats, among other functions. Some tips, recommendations and guidelines for a good experimental analysis of multi-label methods are also presented.},
	doi = {10.1016/j.neucom.2018.02.011},
	author = {Francisco Charte and A.J. Rivera-Rivas and David Charte and M. J. del Jesus and F. Herrera}
}
@conference {332,
	title = {Una primera aproximaci{\'o}n a la predicci{\'o}n de variables tur{\'\i}sticas con Deep Learning},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {TIN2015-68454-R},
	month = {10},
	pages = {939{\textendash}943},
	address = {Granada (Spain)},
	abstract = {El turismo es una de las actividades econ{\'o}micas m{\'a}s importantes a nivel mundial, por lo que una correcta planificaci{\'o}n de los recursos existentes en funci{\'o}n de la demanda es fundamental. En este sentido, el trabajo desarrollado permite comparar la bondad de un nuevo modelo de deep learning, LSTM, frente a un modelo cl{\'a}sico ampliamente reconocido, ARIMA. Se ha llevado a cabo un proceso de entrenamiento para obtener los modelos LSTM y ARIMA que, posteriormente se han validado utilizando datos no disponibles durante el aprendizaje. Nuestros resultados muestran que los nuevos modelos LSTM obtienen una precisi{\'o}n mayor que el cl{\'a}sico ARIMA, tanto en la validaci{\'o}n a priori como en la predicci{\'o}n posterior.},
	isbn = {978-88-61970-00-7},
	author = {Daniel Trujillo Viedma and A.J. Rivera-Rivas and Francisco Charte and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@conference {274,
	title = {Una primera aproximaci{\'o}n para la extracci{\'o}n de patrones emergentes en flujos continuos de datos},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R;BES-2016-077738 },
	pages = {1093-1098},
	address = {Mejor trabajo del II Workshop en Big Data y An{\'a}lisis de Datos Escalable - BigDADE 2018},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {263,
	title = {A First Approach to Handle Emergining Patterns Mining on Big Data Problems: The EvAEFP-Spark Algorithm},
	booktitle = {Proc. of the 2017 IEEE International Conference on Fuzzy Systems},
	year = {2017},
	pages = {1-6},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and M. J. del Jesus and C. J. Carmona}
}
@conference {312,
	title = {A first approach towards a fuzzy decision tree for multilabel classification},
	booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2017},
	note = {TIN2014- 57251-P,P11-TIC-7765},
	month = {7},
	pages = {1{\textendash}6},
	address = {Naples (Italy)},
	abstract = {This paper proposes a multilabel fuzzy decision tree classifier named FuzzDTML. The algorithm uses generalized fuzzy entropy, aggregated over all labels, to choose the best attribute for growing the tree. The proposed algorithm also can generate leaves predicting partial label sets, which can incorporate to some degree the dependence among labels, as well as produce more interpretable models. An empirical analysis shows that, although the algorithm does not yet incorporate pruning nor fuzzy interval adjustment phases, it is competitive with other tree based approaches for multilabel classification, with better performance in data sets having numerical features that can be fuzzified.},
	isbn = {978-1-5090-6034-4},
	doi = {10.1109/FUZZ-IEEE.2017.8015521},
	author = {Prati, Ronaldo C and Francisco Charte and F. Herrera}
}
@article {Mart{\'\i}nez2017,
	title = {A methodology for applying k-nearest neighbor to time series forecasting},
	journal = {Artificial Intelligence Review},
	year = {2017},
	month = {Nov},
	abstract = {In this paper a methodology for applying k-nearest neighbor regression on a time series forecasting context is developed. The goal is to devise an automatic tool, i.e., a tool that can work without human intervention; furthermore, the methodology should be effective and efficient, so that it can be applied to accurately forecast a great number of time series. In order to be incorporated into our methodology, several modeling and preprocessing techniques are analyzed and assessed using the N3 competition data set. One interesting feature of the proposed methodology is that it resolves the selection of important modeling parameters, such as k or the input variables, combining several models with different parameters. In spite of the simplicity of k-NN regression, our methodology seems to be quite effective.},
	issn = {1573-7462},
	doi = {10.1007/s10462-017-9593-z},
	url = {https://doi.org/10.1007/s10462-017-9593-z},
	author = {Francisco Mart{\'\i}nez and Fr{\'\i}as, Mar{\'\i}a Pilar and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@article {627,
	title = {A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets},
	journal = {International Journal of Neural Systems},
	volume = {27},
	year = {2017},
	note = {TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765, UJA2014/06/15},
	pages = {1-17},
	doi = {10.1142/S0129065717500289},
	author = {A. Fern{\'a}ndez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {308,
	title = {A specialized lazy learner for time series forecasting},
	booktitle = {17th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2017},
	year = {2017},
	note = {TIN2015-68854-R},
	month = {7},
	pages = {1397{\textendash}1403},
	address = {Costa Ballena, Rota, C{\'a}adiz (Spain)},
	abstract = {In a time series context the nearest neighbour algorithm looks for the historical observations most similar to the latest observations of the time series. However, some nearest neighbours can be misleading. In this paper we propose that, if prior information about the structure of the time series is known, the search space of possible neighbours can be narrowed so that some possibly misleading neighbours are avoided. This way a more effective forecasting method can be obtained.},
	isbn = {978-84-617-8694-7},
	author = {Francisco Mart{\'\i}nez and M.P. Fr{\'\i}as and Francisco Charte and A.J. Rivera-Rivas}
}
@conference {309,
	title = {A Transformation Approach Towards Big Data Multilabel Decision Trees},
	booktitle = {14th International Work-Conference on Artificial Neural Networks (IWANN 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {73{\textendash}84},
	address = {C{\'a}diz (Spain)},
	abstract = {A large amount of the data processed nowadays is multilabel in nature. This means that every pattern usually belongs to several categories at once. Multilabel data are abundant, and most multilabel datasets are quite large. This causes that many multilabel classification methods struggle with their processing. Tackling this task by means of big data methods seems a logical choice. However, this approach has been scarcely explored by now. The present work introduces several big data multilabel classifiers, all of them based on decision trees. After detailing how they have been designed, their predictive performance, as well as the execution time, are analyzed.},
	isbn = {978-3-319-59152-0},
	doi = {10.1007/978-3-319-59153-7_7},
	author = {A.J. Rivera-Rivas and Francisco Charte and F. Pulgar-Rubio and M. J. del Jesus}
}
@article {COUSO201735,
	title = {Additive similarity and dissimilarity measures},
	journal = {Fuzzy Sets and Systems},
	volume = {322},
	year = {2017},
	note = {Theme: Preference and Similarity},
	pages = {35 - 53},
	abstract = {We develop a detailed formal study of the different variants of the notions of similarity and dissimilarity between fuzzy sets under the assumption of {\textquotedblleft}additivity{\textquotedblright}. A measure of (dis)similarity is said to be additive when it can be decomposed as the sum of the particular (dis)similarities of the compared memberships over the different elements of the universe. Some formal relations between existing formalizations of the notions of {\textquotedblleft}similarity{\textquotedblright} and {\textquotedblleft}dissimilarity{\textquotedblright} arise under this additional assumption. We conclude that, in this particular setting, one may basically distinguish between two main categories of comparison measures in the literature: those that depend on both the intersection and the difference of the compared items and those that only depend on their difference.},
	keywords = {Comparison measure, Dissimilarity measure, Divergence measure, Measure of resemblance, Measure of similitude, Similarity measure},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2016.12.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011416304547},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@conference {265,
	title = {An{\'a}lisis de Diferentes Tipos de Reglas en Sistemas Difusos Evolutivos para Miner{\'\i}a de Patrones Emergentes},
	booktitle = {Proc. of the XII Spanish Conference on Metaheuristics, Evolutive and Bioinspired Algorithms (MAEB 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	pages = {876{\textendash}885},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and M. J. del Jesus}
}
@article {552,
	title = {CommuniMents: A Framework for Detecting Community Based Sentiments for Events},
	journal = {International Journal on Semantic Web and Information Systems},
	volume = {13},
	year = {2017},
	pages = {87-108},
	author = {Jarwar, Muhammad Aslam and Abbasi, Rabeeh Ayaz and Mushtaq, Mubashar and Maqbool, Onaiza and Aljohani, Naif R and Daud, Ali and Alowibdi, Jalal S and J. R. Cano and Garc{\'\i}a, Salvador and Chong, Ilyoung}
}
@article {289,
	title = {Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass},
	journal = {Computers \& Chemical Engineering},
	volume = {101},
	year = {2017},
	note = {ENE2014-60090-C2-2-R,TIN2015-68454-R},
	pages = {23{\textendash}30},
	abstract = {The production of biofuels is a process that requires the adjustment of multiple parameters. Performing experiments in which these parameters are changed and the outputs are analyzed is imperative, but the cost of these tests limits their number. For this reason, it is important to design models that can predict the different outputs with changing inputs, reducing the number of actual experiments to be completed. Response Surface Methodology (RSM) is one of the most common methods for this task, but machine learning algorithms represent an interesting alternative. In the present study the predictive performance of multiple models built from the same problem data are compared: the production of bioethanol from lignocellulosic materials. Four machine learning algorithms, including two neural networks, a support vector machine and a fuzzy system, together with the RSM method, are analyzed. Results show that Reg-CO2RBFN, the method designed by the authors, improves the results of all other alternatives.},
	doi = {10.1016/j.compchemeng.2017.02.008},
	author = {Francisco Charte and Inmaculada Romero and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Eulogio Castro}
}
@article {article,
	title = {Detecting hidden periodicities for models with cyclical errors},
	journal = {Statistics and Its Interface},
	volume = {10},
	number = {1},
	year = {2017},
	month = {04},
	pages = {107-118},
	doi = {10.4310/SII.2017.v10.n1.a10},
	author = {Fr{\'\i}as, M.P. and Ivanov, Alexander and Leonenko, Nikolai and Francisco Mart{\'\i}nez and Dolores Ruiz-Medina, Mar{\'\i}a}
}
@article {299,
	title = {Evoluci{\'o}n tecnol{\'o}gica del hardware de v{\'\i}deo y las GPU en los ordenadores personales},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {7},
	year = {2017},
	note = {-},
	pages = {111{\textendash}128},
	abstract = {This article provides a review of the most important milestones in the evolution of graphics hardware. Communication between computers and people has been advancing over time, reaching interactivity with the emergence of timesharing systems in the early 1960s. Personal computers, whose expansion began almost two decades later, used the visualization of information on a screen as the main means of communication with the user from the very beginning. The hardware in charge of this task has gradually evolved to become an indispensable part of the computer architecture, to such an extent that a large part of laptops and desktop computers incorporate the graphic hardware into the same integrated circuit that houses the microprocessor.},
	issn = {2173-8688},
	author = {Francisco Charte and Rueda, Antonio J. and Espinilla, Macarena and A.J. Rivera-Rivas}
}
@conference {264,
	title = {Impact of the Type of Rule in Fuzzy Emerging Pattern Mining on a Big Data Approach},
	booktitle = {Proc. of the II International symposium on Fuzzy and Rough Sets (ISFUROS 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	author = {A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {290,
	title = {Is the average photon energy a unique characteristic of the spectral distribution of global irradiance?},
	journal = {Solar Energy},
	volume = {149},
	year = {2017},
	note = {ENE2008-05098-ALT},
	pages = {32{\textendash}43},
	abstract = {The average photon energy (APE) has become a popular index to qualitatively assess whether shorter or longer wavelengths are enhanced in a specific spectral distribution of irradiance when compared to the AM1.5G standard spectrum. According to some previous assessments, this index might uniquely distinguish individual global tilted irradiance and global horizontal irradiance spectra. This paper basically applies the same methodology as that used in these studies, i.e., a statistical analysis based on spectral distributions grouped in 0.02-eV APE bins and their standard deviation across all 50-nm bands into which the wavelength range under scrutiny (350 to 1050 nm) is divided. Two years of spectral global tilted irradiance datasets collected at two Spanish locations, 333 km apart, are analyzed here.},
	doi = {10.1016/j.solener.2017.03.086},
	author = {Nofuentes, G and Gueymard, CA and J. Aguilera and M.D. P{\'e}rez-Godoy and Francisco Charte}
}
@article {753,
	title = {KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining.},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {10},
	number = {1},
	year = {2017},
	pages = {1238-1249},
	issn = {1875-6891},
	author = {I. Triguero and S. Gonzalez and J.M. Moyano and S. Garc{\'\i}a and J. Alcal{\'a}-Fdez and J. Luengo and A. Fern{\'a}ndez and M. J. del Jesus and L. S{\'a}nchez and F. Herrera and ATLANTIS PRESS.}
}
@article {602,
	title = {MEFASD-BD: Multi-Objective Evolutionary Algorithm for Subgroup Discovery in Big Data Environments - A MapReduce Solution},
	journal = {Knowledge-Based Systems},
	volume = {117},
	year = {2017},
	note = {TIN2015-68454-R},
	pages = {70-78},
	doi = {http://dx.doi.org/10.1016/j.knosys.2016.08.021},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@conference {8015572,
	title = {Mining association rules in R using the package RKEEL},
	booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2017},
	month = {July},
	pages = {1-6},
	abstract = {The discovery of fuzzy associations comprises a collection of data mining methods used to extract knowledge from large data sets. Although there is an extensive catalog of specialized algorithms that cover different aspects of the problem, the most recent approaches are not yet packaged in mainstream software environments. This makes it difficult to incorporate novel association rules methods to the data mining workflow. In this paper an extension of the RKEEL package is described that allows calling from the programming language R to those association rules methods contained in KEEL, which is one of the most comprehensive open source software suites. The potential of the proposed tool is illustrated through a case study comprising seven real-world datasets.},
	keywords = {Computer science, data mining, data mining methods, data mining workflow, Electronic mail, fuzzy associations, fuzzy set theory, knowledge extraction, large data sets, Measurement, mining association rules, Open source software, open source software suites, programming language R, programming languages, public domain software, real-world datasets, RKEEL package, Software algorithms, software environments, software packages, Tools},
	issn = {1558-4739},
	doi = {10.1109/FUZZ-IEEE.2017.8015572},
	author = {O. S{\'a}nchez and J. M. Moyano and L. S{\'a}nchez and J. Alc{\'a}la-F{\'a}dez}
}
@conference {310,
	title = {Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO2RBFN},
	booktitle = {14th International Work-Conference on Artificial Neural Networks (IWANN 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {733{\textendash}744},
	address = {C{\'a}diz (Spain)},
	abstract = {Research in renewable energies is a global trend. One remarkable area is the biomass transformation into biotehanol, a fuel that can replace fossil fuels. A key step in this process is the pretreatment stage, where several variables are involved. The experimentation for determining the optimal values of these variables is expensive, therefore it is necessary to model this process. This paper focus on modeling the production of biotehanol from olive tree biomass by data mining methods. Notably, the authors present Reg-CO2RBFN, an adaptation of a cooperative-competitive designing method for radial basis function networks. One of the main drawbacks in this modeling is the low number of instances in the data sets. To compare the results obtained by Reg-CO2RBFN, other well-known data mining regression methods are used to model the transformation process.},
	isbn = {978-3-319-59152-0},
	doi = {10.1007/978-3-319-59153-7_63},
	author = {Francisco Charte and Inmaculada Romero and A.J. Rivera-Rivas and Eulogio Castro}
}
@article {Garc{\'\i}a2017,
	title = {MoNGEL: monotonic nested generalized exemplar learning},
	journal = {Pattern Analysis and Applications},
	volume = {20},
	number = {2},
	year = {2017},
	month = {May},
	pages = {441{\textendash}452},
	abstract = {In supervised prediction problems, the response attribute depends on certain explanatory attributes. Some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. In this paper, we aim at formalizing the approach to nested generalized exemplar learning with monotonicity constraints, proposing the monotonic nested generalized exemplar learning (MoNGEL) method. It accomplishes learning by storing objects in {\$}{\$}{\{}{\backslash}mathbb {\{}R{\}}{\}}^n{\$}{\$}Rn, hybridizing instance-based learning and rule learning into a combined model. An experimental analysis is carried out over a wide range of monotonic data sets. The results obtained have been verified by non-parametric statistical tests and show that MoNGEL outperforms well-known techniques for monotonic classification, such as ordinal learning model, ordinal stochastic dominance learner and k-nearest neighbor, considering accuracy, mean absolute error and simplicity of constructed models.},
	issn = {1433-755X},
	doi = {10.1007/s10044-015-0506-y},
	url = {https://doi.org/10.1007/s10044-015-0506-y},
	author = {Javier Garc{\'\i}a and Fardoun, Habib M. and Alghazzawi, Daniyal M. and J. R. Cano and S. Garc{\'\i}a}
}
@conference {311,
	title = {On the Impact of Imbalanced Data in Convolutional Neural Networks Performance},
	booktitle = {12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {220{\textendash}232},
	address = {La Rioja (Spain)},
	abstract = {In recent years, new proposals have emerged for tackling the classification problem based on Deep Learning (DL) techniques. These proposals have shown good results in certain fields, such as image recognition. However, there are factors that must be analyzed to determine how they influence the results obtained by these new algorithms. In this paper, the classification of imbalanced data with convolutional neural networks (CNNs) is analyzed. To do this, a series of tests will be performed in which the classification of real images of traffic signals by CNNs will be performed based on data with different imbalance levels.},
	isbn = {978-3-319-59650-1},
	doi = {10.1007/978-3-319-59650-1_19},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@article {CANO2017128,
	title = {Prototype selection to improve monotonic nearest neighbor},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {60},
	year = {2017},
	pages = {128 - 135},
	abstract = {Student surveys occupy a central place in the evaluation of courses at teaching institutions. At the end of each course, students are requested to evaluate various aspects such as activities, methodology, coordination or resources used. In addition, a final qualification is given to summarize the quality of the course. The prediction of this final qualification can be accomplished by using monotonic classification techniques. The outcome offered by these surveys is particularly significant for faculty and teaching staff associated with the course. The monotonic nearest neighbor classifier is one of the most relevant algorithms in monotonic classification. However, it does suffer from two drawbacks, (a) inefficient execution time in classification and (b) sensitivity to no monotonic examples. Prototype selection is a data reduction process for classification based on nearest neighbor that can be used to alleviate these problems. This paper proposes a prototype selection algorithm called Monotonic Iterative Prototype Selection (MONIPS) algorithm. Our objective is two-fold. The first one is to introduce MONIPS as a method for obtaining monotonic solutions. MONIPS has proved to be competitive with classical prototype selection solutions adapted to monotonic domain. Besides, to further demonstrate the good performance of MONIPS in the context of a student survey about taught courses.},
	keywords = {Data reduction, Monotone nearest neighbor, Monotonic classification, Opinion surveys, Prototype selection},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2017.02.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197617300295},
	author = {J. R. Cano and Naif R. Aljohani and Rabeeh Ayaz Abbasi and Jalal S. Alowidbi and S. Garc{\'\i}a}
}
@article {doi:10.1002/isaf.1409,
	title = {Time series forecasting using evolutionary neural nets implemented in a volunteer computing system},
	journal = {Intelligent Systems in Accounting, Finance and Management},
	volume = {24},
	number = {2-3},
	year = {2017},
	pages = {87-95},
	abstract = {Summary jsEvRBF is a time-series forecasting method based on genetic algorithm and neural nets. Written in JavaScript language, can be executed in most web browsers. Consequently, everybody can participate in the experiments, and scientists can take advantage of nowadays available browsers and devices as computation environments. This is also a great challenge as the language support and performance varies from one browser to another. In this paper, jsEvRBF has been tested in a volunteer computing experiment, and also in a single-browser one. Both experiments are related to forecasting currencies exchange, and the results show the viability of the proposal.},
	keywords = {evolutionary computation, fintech, radial basis function neural networks, time-series forecasting, volunteer computation, Web-based programming},
	doi = {10.1002/isaf.1409},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/isaf.1409},
	author = {Rivas, V.M. and E. Parras-Guti{\'e}rrez and Merelo, J.J. and Arenas, M.G. and Garc{\'\i}a-Fern{\'a}ndez, P.}
}
@article {CANO201794,
	title = {Training set selection for monotonic ordinal classification},
	journal = {Data \& Knowledge Engineering},
	volume = {112},
	year = {2017},
	pages = {94 - 105},
	abstract = {In recent years, monotonic ordinal classification has increased the focus of attention for machine learning community. Real life problems frequently have monotonicity constraints. Many of the monotonic classifiers require that the input data sets satisfy the monotonicity relationships between its samples. To address this, a conventional strategy consists of relabeling the input data to achieve complete monotonicity. As an alternative, we explore the use of preprocessing algorithms without modifying the class label of the input data. In this paper we propose the use of training set selection to choose the most effective instances which lead the monotonic classifiers to obtain more accurate and efficient models, fulfilling the monotonic constraints. To show the benefits of our proposed training set selection algorithm, called MonTSS, we carry out an experimentation over 30 data sets related to ordinal classification problems.},
	keywords = {Data preprocessing, machine learning, Monotonic classification, Ordinal classification, Training set selection},
	issn = {0169-023X},
	doi = {https://doi.org/10.1016/j.datak.2017.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0169023X16303585},
	author = {J. R. Cano and S. Garc{\'\i}a}
}
@article {551,
	title = {Using fuzzy preference orderings in theta-dominance with application to health monitoring of Li-ion batteries},
	journal = {Journal of Multiple-Valued Logic and Soft Computing},
	year = {2017},
	author = {Echevarr{\'\i}a, Yuviny and Couso, In{\'e}s and Anse{\'a}n, David and Blanco, Cecilio and S{\'a}nchez, Luciano}
}
@article {300,
	title = {Uso de dispositivos FPGA como apoyo a la ense{\~n}anza de asignaturas de arquitectura de computadores},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {7},
	year = {2017},
	note = {-},
	pages = {37{\textendash}52},
	abstract = {Computer Engineering students in Spanish universities have to take one or more courses devoted to theur learning of computer architecture. The theoretical part of these subjects are usually focused on describing the architecture itself, while practical sessions are used to introduce assembly programming by means of a certain instruction set which runs into a software emulator. This paper proposes to supplement practical sessions, so that students learn to design a microprocessor by themselves from its basic components, by introducing the use of FPGA devices.},
	issn = {2173-8688},
	author = {Francisco Charte and Espinilla, Macarena and A.J. Rivera-Rivas and F. Pulgar-Rubio}
}
@conference {GarciaCG16,
	title = {A Nearest Hyperrectangle Monotonic Learning Method},
	booktitle = {Proceedings of the 11th International Conference Hybrid Artificial Intelligent Systems, 2016, Seville, Spain, April 18-20, 2016},
	year = {2016},
	pages = {311{\textendash}322},
	author = {Javier Garc{\'\i}a and J. R. Cano and S. Garc{\'\i}a}
}
@article {599,
	title = {A View on Fuzzy Systems for Big Data: Progress and Opportunities},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {9},
	number = {1},
	year = {2016},
	note = {TIN2014-57251-P, P11-TIC-7765, UJA2014/06/15},
	pages = {69-80},
	author = {A. Fern{\'a}ndez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@article {PALACIOS201660,
	title = {An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia},
	journal = {Neurocomputing},
	volume = {176},
	year = {2016},
	note = {Recent Advancements in Hybrid Artificial Intelligence Systems and its Application to Real-World Problems},
	pages = {60 - 71},
	abstract = {An early detection and reeducation of dyslexic children is critical for their integration in the classroom. Parents and instructors can help the psychologist to detect potential cases of dyslexia before the children׳s writing age. Artificial intelligence tools can also assist in this task. Dyslexia symptoms are detected with tests whose results may be vague or ambiguous, thus machine learning techniques for low quality data are advised. In particular, in this paper it is suggested that a new extension to vague datasets of the classification algorithm FURIA (Fuzzy Unordered Rule Induction Algorithm) has advantages over other approaches in both the computational effort during the learning stage and the linguistic quality of the induced classification rules. The new approach is benchmarked with different test problems and compared to other artificial intelligence tools for dyslexia diagnosis in the literature.},
	keywords = {Dyslexia, FURIA, Fuzzy rule based classifiers, Low Quality Data},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2014.11.088},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231215005706},
	author = {Ana Palacios and Luciano S{\'a}nchez and In{\'e}s Couso and S{\'e}bastien Destercke}
}
@conference {329,
	title = {An{\'a}lisis visual de t{\'e}cnicas no supervisadas de deep learning con el paquete dlvisR},
	booktitle = {XVII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2016)},
	year = {2016},
	note = {-},
	month = {9},
	pages = {895{\textendash}904},
	address = {Salamanca (Spain)},
	abstract = {Las t{\'e}cnicas de deep learning aplicadas al aprendizaje no supervisado han demostrado su utilidad y potencial, pero carecen del nivel de interpretabilidad que pueden proporcionar otros algoritmos. Adem{\'a}s, el ajuste de los par{\'a}metros de funcionamiento de este tipo de m{\'e}todos suele realizarse de forma autom{\'a}tica, y no se obtienen explicaciones de c{\'o}mo influyen en el comportamiento de los modelos y los resultados que estos ofrecen. En este trabajo se presenta una herramienta desarrollada para la plataforma R, el paquete dlvisR. Este proporciona un conjunto de utilidades para la visualizaci{\'o}n de las variables obtenidas internamente por este tipo de modelos respecto de par{\'a}metros ajustables por el usuario. Adem{\'a}s, un estudio sobre algunos conjuntos de datos reafirma la hip{\'o}tesis de que la modificaci{\'o}n de dichos par{\'a}metros tiene consecuencias observables visualmente, cuyo an{\'a}lisis podr{\'\i}a aportar conocimiento de inter{\'e}s.},
	isbn = {978-84-9012-632-5},
	author = {David Charte and Francisco Charte and F. Herrera}
}
@conference {603,
	title = {Analysing Concentrating Photovoltaics Technology through the use of Emerging Pattern Mining},
	booktitle = {Proceedings of the 11th International Conference on Soft Computing Models in Industrial and Environmental Applications},
	year = {2016},
	note = {ENE2009-08302, P09-TEP-5045, TIN2015-68454-R},
	author = {A.M. Garc{\'\i}a-Vico and J. Montes and J. Aguilera and C. J. Carmona and M. J. del Jesus}
}
@conference {10.1007/978-3-319-32034-2_21,
	title = {Assessment of Multi-Objective Optimization Algorithms for Parametric Identification of a Li-Ion Battery Model},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2016},
	pages = {250{\textendash}260},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The identification of intelligent models of Li-Ion batteries is a major issue in Electrical Vehicular Technology. On the one hand, the fitness of such models depends on the recursive evaluation of a set of nonlinear differential equations over a representative path in the state space, which is a time consuming task. On the other hand, battery models are intrinsically unstable, and small differences in the initial state or the system, as well as imprecisions in the parameter values, may trigger large differences in the output. Hence, learning battery models from data is a complex multi-modal problem and the parameters of these models must be determined with a high accuracy. In addition to this, producing a dynamical model of a battery is a multi-criteria problem, because the predictive capabilities of the model must be estimated in both the voltage and the temperature domains. In this paper, a selection of state-of-the-art Multi-Objective Optimization Algorithms (SPEA2, NSGA-II, OMOPSO, NSGA-III and MOEA/D) are assessed with regard to their suitability for identifying a model of a Li-Ion battery. The dominance relations that occur between the Pareto fronts are discussed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}ϵ-quality indicators. It is concluded that each of the standard implementations of these algorithms has different issues with this particular problem, MOEA/D and NSGA-III being the best overall alternatives.},
	isbn = {978-3-319-32034-2},
	author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
	editor = {Mart{\'\i}nez-{\'A}lvarez, Francisco and Troncoso, Alicia and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {7737717,
	title = {Battery diagnosis for electrical vehicles through semi-physical fuzzy models},
	booktitle = {2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2016},
	month = {July},
	pages = {416-423},
	abstract = {An intelligent model of a Li-Ion battery for electrical vehicles is proposed that allows for a fast battery health evaluation without the need of removing the battery from the vehicle. The only data needed for performing the condition monitoring are logged performance records (currents and voltages) that are commonly available at Battery Management Systems. The model comprises a combination of differential equations and fuzzy rule-based systems, these last being embedded as non-linear blocks in the differential equations. Fuzzy rules are learnt from data with the help of metaheuristics and fine tuned with gradient descent algorithms. A TensorFlow{\texttrademark} implementation of the learning algorithm has been developed that takes advantage of the numerical processing capabilities of a massively parallel GPU and improves the learning speed by a high margin.},
	keywords = {Batteries, battery diagnosis, battery health evaluation, battery management systems, Battery models, battery powered vehicles, Computational modeling, differential equations, Discharges (electric), electrical vehicles, fuzzy models, Fuzzy rule-based systems, Fuzzy systems, gradient descent algorithms, graphics processing units, learning (artificial intelligence), learning algorithm, Li-FePO4batteries, Li-ion battery, Liquids, massively parallel GPU, Mathematical model, nonlinear blocks, Numerical models, parallel processing, power engineering computing, secondary cells, semiphysical fuzzy models, TensorFlow, TensorFlow{\texttrademark}, Vehicles},
	doi = {10.1109/FUZZ-IEEE.2016.7737717},
	author = {L. S{\'a}nchez and J. Otero and I. Couso and C. Blanco}
}
@conference {313,
	title = {Combining simple exponential smoothing models for time series forecasting},
	booktitle = {International work-conference on Time Series, ITISE 2016},
	year = {2016},
	note = {-},
	month = {6},
	pages = {635-644},
	address = {Granada (Spain)},
	abstract = {Simple exponentional smoothing is a well-known technique for forecasting univariate time series without trend and seasonality. Forecast combinations such as medians or means are known to improve the accuracy of point forecasts. In this paper we have experimented with combining the forecasts of several simple exponential smoothing models with different smoothing factors. Experimental results, using the M3-competition time series, show that the combined forecasts outperform the forecasts of the model that best fits the series.},
	author = {Francisco Mart{\'\i}nez and M.D. P{\'e}rez-Godoy and Francisco Charte and M. J. del Jesus}
}
@conference {600,
	title = {Estimating the Maximum Power Delivered by Concentrating Photovoltaics Technology Through Atmospheric Conditions Using a Differential Evolution Approach},
	booktitle = {Proceedings of the Eleventh International Conference on Hybrid Artificial Intelligence Systems (HAIS)},
	year = {2016},
	note = {ENE2009-08302, P09-TEP-5045, TIN2015-68454-R},
	month = {April},
	pages = {273-282},
	publisher = {Springer},
	organization = {Springer},
	address = {Sevilla (Spain)},
	author = {C. J. Carmona and F. Pulgar-Rubio and A.J. Rivera-Rivas and M. J. del Jesus and J. Aguilera}
}
@article {301,
	title = {Explotaci{\'o}n de la potencia de procesamiento mediante paralelismo: un recorrido hist{\'o}rico hasta la GPGPU},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {6},
	year = {2016},
	note = {-},
	pages = {19{\textendash}33},
	abstract = {Due to the improvement of semiconductor manufacturing technologies, and higher integration scales in the last decades, the power of computing devices has experienced an impressive growth. However, some obstacles have been also found along the way. As a consequence, the battle for reaching higher clock frequencies almost ended a few years ago. Nowadays, the power of processors is not measured exclusively using GHz. Other factors, as the number of cores and their inner design, also have a large impact. This paper provides an historical review on how parallelism techniques have been adapted over time to overcome these changes aiming to better exploit the available hardware.},
	issn = {2173-8688},
	author = {Francisco Charte and A.J. Rivera-Rivas and F. Pulgar-Rubio and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@article {OTERO201642,
	title = {Finding informative code metrics under uncertainty for predicting the pass rate of online courses},
	journal = {Information Sciences},
	volume = {373},
	year = {2016},
	pages = {42 - 56},
	abstract = {A method is proposed for predicting the pass rate of a Computer Science course. Input data comprises different software metrics that are evaluated on a set of programs, comprising students{\textquoteright} answers to a list of computing challenges proposed by the course instructor. Different kinds of uncertainty are accepted, including missing answers and multiple responses to the same challenge. The most informative metrics are selected according to an extension to vague data of the observed Fisher information. The proposed method was tested on experimental data collected during two years at Oviedo University. Yearly changes in the pass rate of two groups were accurately predicted on the basis of 7 software metrics. 73 volunteer students and 1500 source files were involved in the experimentation.},
	keywords = {Automatic grading, feature selection, Genetic Fuzzy Systems, Low Quality Data, vague data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2016.08.090},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025516306715},
	author = {Jos{\'e} Otero and Luis Junco and Rosario Su{\'a}rez and Ana Palacios and In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {554,
	title = {Gamificaci{\'o}n en procesos de autoentrenamiento y autoevaluaci{\'o}n. Experiencia en la asignatura de Arquitectura de Computadores},
	volume = {6},
	year = {2016},
	pages = {55-65},
	abstract = {En la educaci{\'o}n, la gamificaci{\'o}n de procesos est{\'a} suponiendo una excelente soluci{\'o}n para aumentar la motivaci{\'o}n del alumnado para desempe{\~n}ar las actividades de su aprendizaje. As{\'\i}, en dicho entorno, la gamificaci{\'o}n se traduce en brindar a los estudiantes una motivaci{\'o}n adicional inmediata que les permita alcanzar una tarea a largo plazo. Nuestro inter{\'e}s en este trabajo se enfoca en compartir la experiencia del uso de din{\'a}micas y mecanismos de juego para incentivar el proceso de aprendizaje aut{\'o}nomo del alumno a trav{\'e}s de los procesos de autoentrenamiento y autoevaluaci{\'o}n en la preparaci{\'o}n de una prueba objetiva para la asignatura de Arquitectura de Computadores en los estudios de Ingenier{\'\i}a en Inform{\'a}tica de la Universidad de Ja{\'e}n.},
	issn = {2173-8688},
	author = {Espinilla, M. and Santamar{\'\i}a, J and A.J. Rivera-Rivas}
}
@conference {10.1007/978-3-319-44636-3_29,
	title = {Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering},
	booktitle = {Advances in Artificial Intelligence},
	year = {2016},
	pages = {310{\textendash}320},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Learning semi-physical fuzzy models of rechargeable Li-Ion batteries from data involves solving a complex multicriteria optimization task where the accuracies of the approximations of the different observable variables are balanced. The fitness function of this problem depends on the recursive evaluation of a set of differential equations, where fuzzy rule-based systems are embedded as nonlinear blocks. Evaluating this function is a time consuming process, thus algorithms that efficiently promote diversity and hence demand a low number of evaluations of the fitness function are preferred. In this paper, a comparison is carried out between some recent genetic algorithms, whose performances are assessed in this particular modelling problem. It is concluded that the combination of the recent {\$}{\$}{\backslash}theta {\$}{\$}-Dominance Evolutionary Algorithm ({\$}{\$}{\backslash}theta {\$}{\$}-DEA) with a Knowledge-based precedence operator, that improves the selection, is a sensible choice. Dominance relations between the Pareto fronts are assessed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}-quality indicators.},
	isbn = {978-3-319-44636-3},
	author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
	editor = {Luaces , Oscar and G{\'a}mez, Jos{\'e} A. and Barrenechea, Edurne and Troncoso, Alicia and Galar, Mikel and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@article {GarciaAACG16,
	title = {Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms},
	journal = {International Journal Computational Intelligence Systems},
	volume = {9},
	number = {1},
	year = {2016},
	pages = {184{\textendash}201},
	doi = {10.1080/18756891.2016.1146536},
	author = {Javier Garc{\'\i}a and Adnan Al-bar and Naif R. Aljohani and J. R. Cano and S. Garc{\'\i}a}
}
@article {article,
	title = {Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster},
	journal = {Energies},
	volume = {9},
	year = {2016},
	month = {03},
	pages = {197},
	doi = {10.3390/en9030197},
	author = {Coca{\~n}a-Fern{\'a}ndez, Alberto and S{\'a}nchez, Luciano and Ranilla, Josr}
}
@article {COCANAFERNANDEZ201695,
	title = {Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {48},
	year = {2016},
	pages = {95 - 105},
	abstract = {A proactive mechanism to learn an efficient strategy for adaptive resource clusters is proposed. In contrast to reactive techniques, that rescale the cluster to fit the past load, a predictive strategy is adopted. The cluster incoming workload is forecasted and an optimization problem is defined whose solution is the optimal action according to a utility function. Genetic-based machine learning techniques are used, including multi-objective evolutionary algorithms under the distal supervised learning setup. Experimental evaluations show that the proactive system presented in this work improves either the energetic efficiency or the number of reconfigurations of previous approaches without a loss in the quality of service. Depending on the predictability of the workload, in real world cluster scenarios additional energy savings of up to approximately 40\% were measured over the best previous approach, with a 2{\texttimes} factor increment in the number of reconfigurations.},
	keywords = {Distal learning, Energy-efficient cluster computing, Evolutionary algorithms, Multi-criteria decision making},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2015.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197615002183},
	author = {Alberto Coca{\~n}a-Fern{\'a}ndez and Luciano S{\'a}nchez and Jos{\'e} Ranilla}
}
@article {COUSO2016129,
	title = {Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach},
	journal = {Information Sciences},
	volume = {358-359},
	year = {2016},
	pages = {129 - 150},
	abstract = {We study those problems where the goal is to find {\textquotedblleft}optimal{\textquotedblright} models with respect to some specific criterion, in regression and supervised classification problems. Alternatives to the usual expected loss minimization criterion are proposed, and a general framework where this criterion can be seen as a particular instance of a general family of criteria is provided. In the new setting, each model is formally identified with a random variable that associates a loss value to each individual in the population. Based on this identification, different stochastic orderings between random variables lead to different criteria to compare pairs of models. Our general setting encompasses the classical criterion based on the minimization of the expected loss, but also other criteria where a numerical loss function is not available, and therefore the computation of its expectation does not make sense. The presentation of the new framework is divided into two stages. First, we consider the new framework under standard situations about the sample information, where both the collection of attributes and the response variables are observed with precision. Then, we assume that just incomplete information about them (expressed in terms of set-valued data sets) is provided. We cast some comparison criteria from the recent literature on learning methods from low-quality data as particular instances of our general approach.},
	keywords = {classification, Generalized stochastic ordering, Loss function, Low-quality data, Regression, Set-valued data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2016.04.016},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025516302511},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@conference {604,
	title = {Miner{\'\i}a de Patrones Emergentes: Una oportunidad para la extracci{\'o}n evolutiva de conocimiento},
	booktitle = {XI Congreso Espa{\~n}ol de Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)},
	year = {2016},
	note = {TIN2015-68454-R},
	author = {A.M. Garc{\'\i}a-Vico and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {330,
	title = {MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation},
	booktitle = {XVII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2016)},
	year = {2016},
	note = {-},
	month = {9},
	pages = {821{\textendash}822},
	address = {Salamanca (Spain)},
	abstract = {This is a summary of our article published in Knowledge-Based Systems to be part of the MultiConference CAEPIA{\textquoteright}16 Key-Works.},
	isbn = {978-84-9012-632-5},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@book {303,
	title = {Multilabel Classification: Problem Analysis, Metrics and Techniques},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: -	The special characteristics of multi-labeled data and the metrics available to measure them. -	The importance of taking advantage of label correlations to improve the results. -	The different approaches followed to face multi-label classification. -	The preprocessing techniques applicable to multi-label datasets. -	The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.},
	isbn = {978-3-319-41111-8},
	doi = {10.1007/978-3-319-41111-8},
	author = {F. Herrera and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {314,
	title = {On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance},
	booktitle = {11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016},
	year = {2016},
	note = {TIN2014-57251-P,TIN2012-33856,P10-TIC-06858,P11-TIC-7765},
	month = {4},
	pages = {500{\textendash}511},
	address = {Seville (Spain)},
	abstract = {Multilabel classification (MLC) is an increasingly widespread data mining technique. Its goal is to categorize patterns in several non-exclusive groups, and it is applied in fields such as news categorization, image labeling and music classification. Comparatively speaking, MLC is a more complex task than multiclass and binary classification, since the classifier must learn the presence of various outputs at once from the same set of predictive variables. The own nature of the data the classifier has to deal with implies a certain complexity degree. How to measure this complexness level strictly from the data characteristics would be an interesting objective. At the same time, the strategy used to partition the data also influences the sample patterns the algorithm has at its disposal to train the classifier. In MLC random sampling is commonly used to accomplish this task. This paper introduces TCS (Theoretical Complexity Score), a new characterization metric aimed to assess the intrinsic complexity of a multilabel dataset, as well as a novel stratified sampling method specifically designed to fit the traits of multilabeled data. A detailed description of both proposals is provided, along with empirical results of their suitability for their respective duties.},
	isbn = {978-3-319-32033-5},
	doi = {10.1007/978-3-319-32034-2_42},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {315,
	title = {R Ultimate Multilabel Dataset Repository},
	booktitle = {11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016},
	year = {2016},
	note = {TIN2014-57251-P,TIN2012-33856,P10-TIC-06858,P11-TIC-7765},
	month = {4},
	pages = {487{\textendash}499},
	address = {Seville (Spain)},
	abstract = {Multilabeled data is everywhere on the Internet. From news on digital media and entries published in blogs, to videos hosted in Youtube, every object is usually tagged with a set of labels. This way they can be categorized into several non-exclusive groups. However, publicly available multilabel datasets (MLDs) are not so common. There is a handful of websites providing a few of them, using disparate file formats. Finding proper MLDs, converting them into the correct format and locating the appropriate bibliographic data to cite them are some of the difficulties usually confronted by researchers and practitioners. In this paper RUMDR (R Ultimate Multilabel Dataset Repository), a new multilabel dataset repository aimed to fuse all public MLDs, is introduced, along with mldr.datasets, an R package which eases the process of retrieving MLDs and their bibliographic information, exporting them to the desired file formats and partitioning them.},
	isbn = {978-3-319-32033-5},
	doi = {10.1007/978-3-319-32034-2_41},
	author = {Francisco Charte and David Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/978-3-319-48799-1_8,
	title = {Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity},
	booktitle = {Ubiquitous Computing and Ambient Intelligence},
	year = {2016},
	pages = {64{\textendash}74},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In our current work we propose a strategy to reduce the vast amounts of data produced within smart environments for sensor-based activity recognition through usage of the nearest neighbor (NN) approach. This approach has a number of disadvantages when deployed in resource constrained environments due to its high storage requirements and computational complexity. These requirements are closely related to the size of the data used as input to NN. A wide range of prototype generation (PG) algorithms, which are designed for use with the NN approach, have been proposed in the literature to reduce the size of the data set. In this work, we investigate the use of PG algorithms and their effect on binary sensor-based activity recognition when using a NN approach. To identify the most suitable PG algorithm four datasets were used consisting of binary sensor data and their associated class activities. The results obtained demonstrated the potential of three PG algorithms for sensor-based activity recognition that reduced the computational complexity by up~to 95~{\%} with an overall accuracy higher than 90~{\%}.},
	isbn = {978-3-319-48799-1},
	author = {Espinilla, M. and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and Medina, J. and Mart{\'\i}nez, L. and Nugent, C.},
	editor = {Garc{\'\i}a, Carmelo R. and Caballero-Gil, Pino and Burmester, Mike and Quesada-Arencibia, Alexis}
}
@conference {inproceedings,
	title = {RKEEL: Using KEEL in R code},
	year = {2016},
	month = {07},
	pages = {257-264},
	doi = {10.1109/FUZZ-IEEE.2016.7737695},
	author = {Moyano, Jose and S{\'a}nchez, Luciano}
}
@article {605,
	title = {Subgroup Discovery with Evolutionary Fuzzy Systems in R: the SDEFSR Package},
	journal = {The R Journal},
	volume = {8},
	year = {2016},
	note = {TIN2015-68854-R},
	pages = {307-323},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {269,
	title = {Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art},
	year = {2016},
	pages = {1-11},
	author = {C. J. Carmona and D. Elizondo}
}
@article {262,
	title = {The Influence of Noise on the Evolutionary Fuzzy Systems for Subgroup Discovery},
	journal = {Soft Computing},
	volume = {20},
	year = {2016},
	note = {TIN2015-68454-R, TIN2014-57251-P, P11-TIC-7765, P12-TIC-2958},
	pages = {4313-4330},
	doi = {10.1007/s00500-016-2300-1},
	author = {J. Luengo and A.M. Garc{\'\i}a-Vico and M.D. P{\'e}rez-Godoy and C. J. Carmona}
}
@conference {10.1007/978-3-319-40111-9_5,
	title = {Web Browser-Based Forecasting of Economic Time-Series},
	booktitle = {Decision Economics, In Commemoration of the Birth Centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978)},
	year = {2016},
	pages = {35{\textendash}42},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents the implementation of a time series forecasting algorithm, jsEvRBF, that uses genetic algorithm and neural nets in a way that can be run in must modern web browsers. Using browsers to run forecasting algorithms is a challenge, since language support and performance varies across implementations of the JavaScript virtual machine and vendor. However, their use will provide a boost in the number of platforms available for scientists. jsEvRBF is written in JavaScript, so that it can be easily delivered to and executed by any device containing a web-browser just accessing an URL. The experiments show the results yielded by the algorithm over a data set related to currencies exchange. Best results achieved can be effectively compared against previous results in literature, though robustness of the new algorithm has to be improved.},
	isbn = {978-3-319-40111-9},
	author = {Rivas, V. M. and E. Parras-Guti{\'e}rrez and Merelo, J. J. and Arenas, M. G. and Garc{\'\i}a-Fern{\'a}ndez, P.},
	editor = {Bucciarelli, Edgardo and Silvestri, Marcello and Rodr{\'\i}guez Gonz{\'a}lez, Sara}
}
@article {Gcrda15,
	title = {A differential evolution proposal for estimating the maximum power delivered by CPV modules under real outdoor conditions},
	journal = {Expert Systems with Applications},
	volume = {42},
	number = {13},
	year = {2015},
	note = {ENE2009-08302, P09-TEP-5045, TIN2012-33856},
	pages = {5452{\textendash}5462},
	doi = {10.1016/j.eswa.2015.02.032},
	author = {B. Garc{\'\i}a-Domingo and C. J. Carmona and A.J. Rivera-Rivas and M. J. del Jesus and J. Aguilera}
}
@inbook {Cl15,
	title = {A first approach in the class noise filtering approaches for Fuzzy Subgroup Discovery},
	booktitle = {Advances in Intelligent Systems and Computing},
	volume = {368},
	year = {2015},
	note = {TIN2012-33856, TIN2011-28488, TIN2010-15055, P10-TIC-6858, P12-TIC-2958},
	pages = {387-399},
	publisher = {Springer},
	organization = {Springer},
	author = {C. J. Carmona and J. Luengo}
}
@article {542,
	title = {A fuzzy genetic programming-based algorithm for subgroup discovery and the application to one problem of pathogenesis of acute sore throat conditions in humans},
	journal = {Information Sciences},
	volume = {298},
	year = {2015},
	note = {TIN2012-33856},
	pages = {180-197},
	doi = {http://dx.doi.org/10.1016/j.ins.2014.11.030},
	author = {C. J. Carmona and V. Ruiz-Rodado and M. J. del Jesus and A. Weber and M. Grootveld and P. Gonz{\'a}lez and D. Elizondo}
}
@conference {7338079,
	title = {A software tool to efficiently manage the energy consumption of HPC clusters},
	booktitle = {2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2015},
	month = {Aug},
	pages = {1-8},
	abstract = {Today, High Performance Computing clusters (HPC) are an essential tool owing to they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nonetheless, HPC clusters consume large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, forcing owners to reduce operation costs. In this work we propose a software, named EECluster, to reduce the high energy consumption of HPC clusters. EECluster works with both OGE/SGE and PBS/TORQUE resource management systems and automatically tunes its decision-making mechanism based on a machine learning approach. The quality of the obtained results using this software are evaluated by means of experiments made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects.},
	keywords = {academic-cluster, Computational modeling, Computer architecture, Decision making, decision-making mechanism, EECluster software tool, Fuzzy systems, Genetics, high performance computing clusters, HPC cluster energy consumption, learning (artificial intelligence), machine learning approach, OGE-SGE resource management systems, Oviedo University, parallel processing, PBS-TORQUE resource management systems, power aware computing, resource allocation, Scientific Modelling Cluster, software tools},
	doi = {10.1109/FUZZ-IEEE.2015.7338079},
	author = {A. Coca{\~n}a-Fern{\'a}ndez and L. S{\'a}nchez and J. Ranilla}
}
@article {287,
	title = {Addressing imbalance in multilabel classification: Measures and random resampling algorithms},
	journal = {Neurocomputing},
	volume = {163},
	year = {2015},
	note = {TIN2012-33856,TIN2011-28488,P10-TIC-6858,P11-TIC-7765},
	pages = {3{\textendash}16},
	chapter = {3},
	abstract = {The purpose of this paper is to analyze the imbalanced learning task in the multilabel scenario, aiming to accomplish two different goals. The first one is to present specialized measures directed to assess the imbalance level in multilabel datasets (MLDs). Using these measures we will be able to conclude which MLDs are imbalanced, and therefore would need an appropriate treatment. The second objective is to propose several algorithms designed to reduce the imbalance in MLDs in a classifier-independent way, by means of resampling techniques. Two different approaches to divide the instances in minority and majority groups are studied. One of them considers each label combination as class identifier, whereas the other one performs an individual evaluation of each label imbalance level. A random undersampling and a random oversampling algorithm are proposed for each approach, giving as result four different algorithms. All of them are experimentally tested and their effectiveness is statistically evaluated. From the results obtained, a set of guidelines directed to show when these methods should be applied is also provided.},
	doi = {10.1016/j.neucom.2014.08.091},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/978-3-319-24834-9_5,
	title = {Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection},
	booktitle = {Intelligent Data Engineering and Automated Learning {\textendash} IDEAL 2015},
	year = {2015},
	pages = {36{\textendash}44},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In classification tasks with imbalanced datasets the distribution of examples between the classes is uneven. However, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping between the classes is possibly the most significant one for a correct discrimination between the classes.},
	isbn = {978-3-319-24834-9},
	author = {Fern{\'a}ndez, Alberto and M. J. del Jesus and F. Herrera},
	editor = {Jackowski, Konrad and Burduk, Robert and Walkowiak, Krzysztof and Wozniak, Michal and Yin, Hujun}
}
@conference {317,
	title = {An ensemble strategy for forecasting the extra-virgin olive oil price in Spain},
	booktitle = {International work-conference on Time Series, ITISE 2015},
	year = {2015},
	note = {TIN2012-33856},
	month = {7},
	pages = {506{\textendash}516},
	address = {Granada (Spain)},
	abstract = {Time series prediction is one of the key tasks in data mining, especially in areas such as science, engineering and business. It is possible to distinguish between fundamental analysis and technical analysis while dealing with time series in the business area. Fundamental analysis takes into account different exogenous variables such as expenses, assets or liabilities. Technical analysis summarizes information using technical indicators such as momentums, moving averages or oscillators. The most influential exogenous variables and technical indicators for the olive oil price have been already identified in previous studies. The objective of the present paper is to propose an ensemble strategy, based on dividing this set of exogenous variables and technical indicators into subsets of features for the base models. These base models use CO2RBFN, a cooperative competitive algorithm for RBFNs, as learning algorithm. The obtained results show that the ensemble strategy outperforms both the base models and other classical soft computing methods.},
	isbn = {978-84-16292-20-2},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and Francisco Charte and F. Pulgar-Rubio and M. J. del Jesus}
}
@conference {Cpggj15,
	title = {An{\'a}lisis descriptivo mediante aprendizaje supervisado basado en patrones emergentes},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos},
	year = {2015},
	note = {TIN2012-33856},
	pages = {685-694},
	author = {C. J. Carmona and F. Pulgar-Rubio and A.M. Garc{\'\i}a-Vico and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {316,
	title = {CO2RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design},
	booktitle = {13th International Work-Conference on Artificial Neural Networks (IWANN 2015)},
	year = {2015},
	note = {TIN2012-33856},
	month = {6},
	pages = {361{\textendash}373},
	address = {Palma de Mallorca (Spain)},
	abstract = {The interest in dealing with imbalanced datasets has grown in the last years, since they represent many real world scenarios. Different methods that address imbalance problems can be classified into three categories: data sampling, algorithmic modification and cost-sensitive learning. The fundamentals of the last methodology is to assign higher costs to wrong classification classes with the aim of reducing higher cost errors. In this paper, CO2RBFN-CS, a cooperative-competitive Radial Basis Function Network algorithm that implements cost-sensitivity is presented. Specifically, a cost parameter is introduced in the training stage of the algorithm. This parameter modifies the learning rate of the weights taking into account the right (or wrong) classification of the instance, the type of class (majority or minority) and the imbalance ratio of the data set.},
	isbn = {978-3-319-19257-4},
	doi = {10.1007/978-3-319-19258-1_31},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@conference {10.1007/978-3-319-19719-7_23,
	title = {Energy-Efficient Sound Environment Classifier for Hearing Aids Based on Multi-objective Simulated Annealing Programming},
	booktitle = {10th International Conference on Soft Computing Models in Industrial and Environmental Applications},
	year = {2015},
	pages = {261{\textendash}270},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {A methodology for designing classifiers through multicriteria metaheuristics is introduced. The purpose of the new method is to jointly optimize the expected total classification cost and the energy consumption of the device the classifier is implemented on. A numerical study is provided, where different alternatives are implemented on a hearing aid. This aid is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to the user{\textquoteright}s comfort. The proposed method attains relevant improvements in energy consumption with small to negligible loss in classification accuracy with respect to a selection of algorithms.},
	isbn = {978-3-319-19719-7},
	author = {Coca{\~n}a-Fern{\'a}ndez, Alberto and S{\'a}nchez, Luciano and Ranilla, Jos{\'e} and Gil-Pita, Roberto and Ayll{\'o}n, David},
	editor = {Herrero, {\'A}lvaro and Sedano, Javier and Baruque, Bruno and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {Cgd15,
	title = {FuGePSD: Fuzzy Genetic Programming-based algorithm for Subgroup Discovery},
	booktitle = {16th World Congress of the International Fuzzy Systems Association and 9th Conference of the European Society for Fuzzy Logic and Technology},
	year = {2015},
	note = {TIN2012-33856},
	pages = {1-8},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {654,
	title = {Generalized stochastic orderings applied to the study of perfomance of  machine learning algorithm},
	booktitle = {The 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT)},
	year = {2015},
	month = {06},
	address = {Gij{\'o}n, Asturias(Spain)},
	author = {Couso, In{\'e}s and S{\'a}nchez, Luciano}
}
@article {PALACIOS2015358,
	title = {Genetic learning of the membership functions for mining fuzzy association rules from low quality data},
	journal = {Information Sciences},
	volume = {295},
	year = {2015},
	pages = {358 - 378},
	abstract = {Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are known in advance and it is not an easy task to know a priori the most appropriate fuzzy sets to cover the domains of the variables. In this paper, we propose FARLAT-LQD, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data. To accomplish this, first we perform a genetic learning of the membership functions based on the 3-tuples linguistic representation model to reduce the search space and to learn the most adequate context for each fuzzy partition, maximizing the fuzzy supports and the interpretability measure GM3M in order to preserve the semantic interpretability of the obtained membership functions. Moreover, we propose a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth-LQD, to efficiently mine the fuzzy association rules from inaccurate data considering the learned membership functions in the genetic process. The results obtained over 3 databases of different sizes and kinds of imprecisions demonstrate the effectiveness of the proposed algorithm.},
	keywords = {3-tuples linguistic representation, data mining, Fuzzy association rules, Genetic Fuzzy Systems, GM3M measure, Imprecise data},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2014.10.027},
	url = {http://www.sciencedirect.com/science/article/pii/S002002551401010X},
	author = {Ana Mar{\'\i}a Palacios and Jos{\'e} Luis Palacios and Luciano S{\'a}nchez and Jes{\'u}s Alcal{\'a}-Fdez}
}
@article {OTERO201544,
	title = {Local iterative DLT soft-computing vs. interval-valued stereo calibration and triangulation with uncertainty bounding in 3D reconstruction},
	journal = {Neurocomputing},
	volume = {167},
	year = {2015},
	pages = {44 - 51},
	abstract = {The use of stereo vision for 3D data gathering is affected by constraints in the position of the cameras, the quality of the optical elements and the numerical algorithms for calibration and matching. Also, there is not a wide agreement on the best procedure for bounding the 3D errors within an uncertainty volume. In this work, this problem is solved by implementing the whole set of computations, including calibration and triangulation, with interval data. This is in contrast with previous works that rely on Direct Linear Transform (DLT) as a camera model. To keep better with real lens aberrations, a local iterative modification is proposed that provides an on-demand set of calibration parameters for each 3D point, comprising those nearest in 3D space. In this way, the estimated camera parameters are closely related with camera aberrations at the lens area through which that 3D point is imaged. To further reduce the triangulation uncertainty volume, a Soft Computing approach is proposed that represents each 3D point uncertainty as a cloud of crisp points compatible with interval-valued calibration data. Real data from previous works in related research areas is used to judge whether the new approach improves the precision and accuracy of other crisp and interval-valued estimations without degrading precision, and it is concluded that the new technique is able to significantly improve the uncertainty volumes.},
	keywords = {Camera calibration, Computer vision, Interval-valued, Possibility, Stereo},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2014.11.087},
	url = {http://www.sciencedirect.com/science/article/pii/S0925231215005512},
	author = {Jos{\'e} Otero and Luciano S{\'a}nchez}
}
@conference {328,
	title = {mldr: Paquete R para Exploraci{\'o}n de Datos Multietiqueta},
	booktitle = {XVI Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2015)},
	year = {2015},
	note = {TIN2012-33856,P10-TIC-06858,P11-TIC-9704},
	month = {11},
	pages = {695{\textendash}704},
	address = {Albacete (Spain)},
	abstract = {La clasificaci{\'o}n tradicional de datos trabaja con varios atributos de entrada y uno solo de salida. La clasificaci{\'o}n multietiqueta, por el contrario, implica predecir simult{\'a}neamente m{\'a}s de un atributo de salida, por lo que los algoritmos de clasificaci{\'o}n han de afrontar una tarea considerablemente m{\'a}s compleja. La clasificaci{\'o}n de datos multietiqueta es una labor de creciente importancia, presente en {\'a}mbitos como la categorizaci{\'o}n de textos, el etiquetado de im{\'a}genes o la identificaci{\'o}n de sonidos en un medio. mldr es un paquete R que incorpora una serie de herramientas para cargar y crear datasets multietiqueta, calcular diversas medidas de caracterizaci{\'o}n de los datos, generar varios gr{\'a}ficos que muestran su distribuci{\'o}n, y manipular los datasets para tratar de facilitar la tarea de los algoritmos de preprocesamiento y de clasificaci{\'o}n. Adem{\'a}s, la interfaz gr{\'a}fica de usuario incluida permite que usuarios sin experiencia con R accedan a las mismas funcionalidades.},
	isbn = {978-84-608-4099-2},
	author = {David Charte and Francisco Charte}
}
@article {288,
	title = {MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation},
	journal = {Knowledge-Based Systems},
	volume = {89},
	year = {2015},
	note = {TIN2012-33856,TIN2011-28488,P10-TIC-6858,P11-TIC-7765},
	pages = {385{\textendash}397},
	chapter = {385},
	abstract = {Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this paper the process of synthetic instance generation for multilabel datasets (MLDs) is studied and MLSMOTE (Multilabel Synthetic Minority Over-sampling Technique), a new algorithm aimed to produce synthetic instances for imbalanced MLDs, is proposed. An extensive review on how imbalance in the multilabel context has been tackled in the past is provided, along with a thorough experimental study aimed to verify the benefits of the proposed algorithm. Several multilabel classification algorithms and other multilabel oversampling methods are considered, as well as ensemble-based algorithms for imbalanced multilabel classification. The empirical analysis shows that MLSMOTE is able to improve the classification results produced by existent proposals.},
	doi = {10.1016/j.knosys.2015.07.019},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {Garc{{\'\i}a2015,
	title = {MoNGEL: monotonic nested generalized exemplar learning},
	journal = {Pattern Analysis and Applications},
	year = {2015},
	pages = {1{\textendash}12},
	issn = {1433-755X},
	doi = {10.1007/s10044-015-0506-y},
	author = {Javier Garc{\'\i}a and Fardoun, Habib M. and Alghazzawi, Daniyal M. and J. R. Cano and S. Garc{\'\i}a}
}
@conference {inproceedings,
	title = {Online SOC estimation of Li-FePO4 batteries through an observer of the system state with minimal nonspecificity},
	year = {2015},
	month = {08},
	pages = {1-8},
	doi = {10.1109/FUZZ-IEEE.2015.7337901},
	author = {S{\'a}nchez, Luciano and Couso, In{\'e}s and Blanco, Cecilio}
}
@conference {596,
	title = {Predicci{\'o}n a muy corto plazo de series temporales de volumen de tr{\'a}fico rodado mediante co-evoluci{\'o}n de RNFBR},
	booktitle = {Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2015},
	note = {SPIP2014-01437,PRY142/14,TIN2011-28627-C04,PYR-2014-17,SIPEsCa del Programa Operativo FEDER de Andaluc{{\'\i}a 2007-2013},
	pages = {181-188},
	address = {M{\'e}rida - Almendralejo (Spain)},
	author = {V. M. Rivas and E. Parras-Guti{\'e}rrez and a. Fernandez-Ares and P.A. Castillo and P. Garcia-Fernandez and M.G. Arenas}
}
@conference {318,
	title = {QUINTA: A question tagging assistant to improve the answering ratio in electronic forums},
	booktitle = {IEEE International Conference on Computer as a Tool, EUROCON 2015},
	year = {2015},
	note = {TIN2014-57251-P,TIN2012-33856,P10-TIC-06858,P11-TIC-7765},
	month = {9},
	pages = {1-6},
	address = {Salamanca (Spain)},
	abstract = {The Web is broadly used nowadays to obtain information about almost any topic, from scientific procedures to cooking recipes. Electronic forums are very popular, with thousands of questions asked and answered every day. Correctly tagging the questions posted by users usually produces quicker and better answers by other users and experts. In this paper a prototype of a system aimed to assist the users while tagging their questions is proposed. To accomplish this task, firstly the text of each post is processed to produce a multilabel dataset, then a lazy nearest neighbor multilabel classification algorithm is used to predict the tags on new posts. The obtained results are promising, opening the door to the developing of a full automated system for this task.},
	isbn = {978-1-4799-8569-2},
	doi = {10.1109/EUROCON.2015.7313677},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {319,
	title = {Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels},
	booktitle = {10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015},
	year = {2015},
	note = {TIN2011-28488,TIN2012-33856,P10-TIC-06858,P11-TIC-7765},
	month = {6},
	pages = {489{\textendash}501},
	address = {Bilbao (Spain)},
	abstract = {Multilabel classification is a task that has been broadly studied in late years. However, how to face learning from imbalanced multilabel datasets (MLDs) has only been addressed latterly. In this regard, a few proposals can be found in the literature, most of them based on resampling techniques adapted from the traditional classification field. The success of these methods varies extraordinarily depending on the traits of the chosen MLDs. One of the characteristics which significantly influences the behavior of multilabel resampling algorithms is the joint appearance of minority and majority labels in the same instances. It was demonstrated that MLDs with a high level of concurrence among imbalanced labels could hardly benefit from resampling methods. This paper proposes an original resampling algorithm, called REMEDIAL, which is not based on removing majority instances nor creating minority ones, but on a procedure to decouple highly imbalanced labels. As will be experimentally demonstrated, this is an interesting approach for certain MLDs.},
	isbn = {978-3-319-19643-5},
	doi = {10.1007/978-3-319-19644-2_41},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {FERNANDEZ2015109,
	title = {Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges},
	journal = {Knowledge-Based Systems},
	volume = {80},
	year = {2015},
	note = {25th anniversary of Knowledge-Based Systems},
	pages = {109 - 121},
	abstract = {Evolutionary Fuzzy Systems are a successful hybridization between fuzzy systems and Evolutionary Algorithms. They integrate both the management of imprecision/uncertainty and inherent interpretability of Fuzzy Rule Based Systems, with the learning and adaptation capabilities of evolutionary optimization. Over the years, many different approaches in Evolutionary Fuzzy Systems have been developed for improving the behavior of fuzzy systems, either acting on the Fuzzy Rule Base Systems{\textquoteright} elements, or by defining new approaches for the evolutionary components. All these efforts have enabled Evolutionary Fuzzy Systems to be successfully applied in several areas of Data Mining and engineering. In accordance with the former, a wide number of applications have been also taken advantage of these types of systems. However, with the new advances in computation, novel problems and challenges are raised every day. All these issues motivate researchers to make an effort in releasing new ways of addressing them with Evolutionary Fuzzy Systems. In this paper, we will review the progression of Evolutionary Fuzzy Systems by analyzing their taxonomy and components. We will also stress those problems and applications already tackled by this type of approach. We will present a discussion on the most recent and difficult Data Mining tasks to be addressed, and which are the latest trends in the development of Evolutionary Fuzzy Systems.},
	keywords = {Big Data, data mining, Evolutionary Fuzzy Systems, fuzzy rule based systems, Multi-Objective Evolutionary Fuzzy Systems, New trends, Scalability, Taxonomy},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2015.01.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705115000209},
	author = {Alberto Fernandez and Victoria L{\'o}pez and M. J. del Jesus and F. Herrera}
}
@article {PALACIOS201510,
	title = {Sequential pattern mining applied to aeroengine condition monitoring with uncertain health data},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {44},
	year = {2015},
	pages = {10 - 24},
	abstract = {Numerical algorithms that can assess Engine Health Monitoring (EHM) data in aeroengines are influenced by the high level of uncertainty inherent to gas path measurements and engine-to-engine variability. Among them, fuzzy rule-based techniques have been successfully used due to their robustness towards noisy signals and their capability to learn human-readable rules from data. These techniques are useful in detecting the presence of certain types of abnormal events or general engine deterioration, through the identification of specific combinations of EHM signals associated with these specific cases. However, there are also other types of engine events that manifest themselves as an ordered sequence of otherwise normal combinations of the EHM signals. These combinations are dismissed when considered in isolation as the current existing techniques cannot assess them. In this paper it is proposed to use sequence mining techniques in order to obtain fuzzy rules from uncertain EHM data which can in turn be used to identify the cases where an engine event is determined as a sequence of otherwise normal combinations of EHM signals. The results are subsequently tested on a representative sample of aeroengine data.},
	keywords = {engine health monitoring, Fuzzy rule-based systems, Fuzzy sequence mining, Uncertain data},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2015.05.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197615001062},
	author = {Ana Palacios and Alvaro Mart{\'\i}nez and Luciano S{\'a}nchez and In{\'e}s Couso}
}
@article {626,
	title = {Subgroup Discovery through Evolutionary Fuzzy Systems applied to Bioinformatics problems},
	year = {2015},
	pages = {1-28},
	institution = {De Montfort University},
	address = {Leicester, United Kingdom},
	author = {C. J. Carmona and D. Elizondo}
}
@article {COUSO2015127,
	title = {The behavioral meaning of the median},
	journal = {Information Sciences},
	volume = {294},
	year = {2015},
	note = {Innovative Applications of Artificial Neural Networks in Engineering},
	pages = {127 - 138},
	abstract = {We extend the notion of statistical preference to the general framework of imprecise probabilities, by proposing a new notion of desirability of gambles called {\textquotedblleft}sign-desirability{\textquotedblright}, different from the usual desirability notion in Walley{\textquoteright}s framework. We axiomatically characterize coherent families of sign-desirable gambles. We furthermore prove that the pair of lower and upper previsions of a gamble, according to this new desirability notion, coincides with the pair of bounds (infimum and supremum) of the set of medians associated to a coherent family of linear previsions. Thus, a general notion of median is naturally derived, and provided with a behavioral meaning. As a consequence of these results, the connection between statistical preference in classical Probability Theory, and the sign of the median of the difference of two random variables is laid bare.},
	keywords = {Desirable gamble, Imprecise Probability, Median, Statistical preference, Stochastic ordering},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2014.08.049},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025514008548},
	author = {In{\'e}s Couso and Seraf{\'\i}n Moral and Luciano S{\'a}nchez}
}
@conference {Pcagd15,
	title = {Una primera aproximaci{\'o}n al descubrimiento de subgrupos bajo el paradigma MapReduce},
	booktitle = {1er Workshop en Big Data y An{\'a}lisis de Datos Escalable},
	year = {2015},
	note = {TIN2012-33856},
	pages = {991-1000},
	author = {F. Pulgar-Rubio and C. J. Carmona and A.J. Rivera-Rivas and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {Gcgcj15,
	title = {Usando Algoritmos de Descubrimiento de Subgrupos en R: El Paquete SDR},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos},
	year = {2015},
	note = {TIN2012-33856},
	pages = {739-748},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {296,
	title = {Working with Multilabel Datasets in R: The mldr Package},
	journal = {The R Journal},
	volume = {7},
	number = {2},
	year = {2015},
	note = {TIN2012-33856},
	pages = {149{\textendash}162},
	abstract = {Most classification algorithms deal with datasets which have a set of input features, the variables to be used as predictors, and only one output class, the variable to be predicted. However, in late years many scenarios in which the classifier has to work with several outputs have come to life. Automatic labeling of text documents, image annotation or protein classification are among them. Multilabel datasets are the product of these new needs, and they have many specific traits. The mldr package allows the user to load datasets of this kind, obtain their characteristics, produce specialized plots, and manipulate them. The goal is to provide the exploratory tools needed to analyze multilabel datasets, as well as the transformation and manipulation functions that will make possible to apply binary and multiclass classification models to this data or the development of new multilabel classifiers. Thanks to its integrated user interface, the exploratory functions will be available even to non-specialized R users.},
	doi = {10.32614/RJ-2015-027},
	author = {Francisco Charte and David Charte}
}
@article {SANCHEZ2014269,
	title = {A design methodology for semi-physical fuzzy models applied to the dynamic characterization of LiFePO4 batteries},
	journal = {Applied Soft Computing},
	volume = {14},
	year = {2014},
	note = {Evolving Soft Computing Techniques and Applications},
	pages = {269 - 288},
	abstract = {A methodology for designing semi-physical fuzzy models is proposed. Prior physical knowledge about the dynamics of the system is modeled with continuous time differential equations. Fuzzy knowledge bases are embedded in these equations as nonlinear constructive blocks. Rules comprising the knowledge bases are fitted to interval-valued data with metaheuristics. A possibilistic filter is proposed that is able to gradually evolve an initial estimation of the latent variables of the model on the basis of successive prediction errors. This methodology has been applied to the prediction of voltage and state of charge of LiFePO4 batteries. An empirical study has been carried over data gathered in experiments at the Battery Laboratory at Oviedo University. Fitting between the proposed model and actual measurements is studied for four different manufacturers and different charge{\textendash}discharge patterns. Predictions of the evolution of the voltage during charge, discharge and inactivity compare favorably to different models in the literature. The possibilistic filter allows to estimate the state of charge of batteries after an arbitrary path that may include partial charges and discharges. It is shown that the accuracy of the open loop model improves that of other approaches in the literature, and at the same time the observer-based online model is able to approximate the effective remnant charge of the battery after a reasonably short time.},
	keywords = {fuzzy models, Imprecise data, LiFePO batteries, Online identification, Semi-physical modeling},
	issn = {1568-4946},
	doi = {https://doi.org/10.1016/j.asoc.2013.03.020},
	url = {http://www.sciencedirect.com/science/article/pii/S156849461300135X},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and Manuela Gonz{\'a}lez}
}
@conference {Otero2014APF,
	title = {A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments},
	booktitle = {TheScientificWorldJournal},
	year = {2014},
	author = {Jos{\'e} Varela Otero and Ana M. Palacios and Rosario Su{\'a}rez and Luis Junco and In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {Ltcgh14,
	title = {Addressing Imbalanced Classification with Instance Generation Techniques: IPADE-ID},
	journal = {Neurocomputing},
	volume = {126},
	year = {2014},
	note = {TIN2011-28488,P11-TIC-7765,P10-TIC-6858},
	pages = {15-28},
	doi = {10.1016/j.neucom.2013.01.050},
	author = {V. L{\'o}pez and I. Triguero and C. J. Carmona and S. Garc{\'\i}a and F. Herrera}
}
@conference {655,
	title = {An ensemble method for time series forecasting with simple exponential smoothing},
	booktitle = {Conference Computational and Mathematical Methods in Science and Engineering},
	year = {2014},
	month = {07},
	address = {Rota, C{\'a}diz (Spain)},
	author = {M. J. del Jesus and Francisco Mart{\'\i}nez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fr{\'\i}as, Mar{\'\i}a Pilar}
}
@article {article,
	title = {An Equivalent Circuit Model With Variable Effective Capacity for LiFePO4 Batteries},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {63},
	year = {2014},
	month = {10},
	pages = {3592-3599},
	doi = {10.1109/TVT.2014.2309394},
	author = {Blanco, Cecilio and S{\'a}nchez, Luciano and Gonzalez, M and Ant{\'o}n, Juan Carlos and Garc{\'\i}a Fern{\'a}ndez, V{\'\i}ctor and Viera, Juan}
}
@article {doi:10.1002/widm.1134,
	title = {Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {4},
	number = {5},
	year = {2014},
	pages = {380-409},
	abstract = {The term {\textquoteleft}Big Data{\textquoteright} has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380{\textendash}409. doi: 10.1002/widm.1134 This article is categorized under: Technologies > Classification Technologies > Computer Architectures for Data Mining},
	issn = {1942-4787},
	doi = {10.1002/widm.1134},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1134},
	author = {Fern{\'a}ndez, Alberto and del R{\'\i}o, Sara and L{\'o}pez, Victoria and Bawakid, Abdullah and M. J. del Jesus and Benitez, Jose M. and F. Herrera}
}
@article {OTERO201488,
	title = {Bootstrap analysis of multiple repetitions of experiments using an interval-valued multiple comparison procedure},
	journal = {Journal of Computer and System Sciences},
	volume = {80},
	number = {1},
	year = {2014},
	pages = {88 - 100},
	abstract = {A new bootstrap test is introduced that allows for assessing the significance of the differences between stochastic algorithms in a cross-validation with repeated folds experimental setup. Intervals are used for modeling the variability of the data that can be attributed to the repetition of learning and testing stages over the same folds in cross validation. Numerical experiments are provided that support the following three claims: (1) Bootstrap tests can be more powerful than ANOVA or Friedman test for comparing multiple classifiers. (2) In the presence of outliers, interval-valued bootstrap tests achieve a better discrimination between stochastic algorithms than nonparametric tests. (3) Choosing ANOVA, Friedman or Bootstrap can produce different conclusions in experiments involving actual data from machine learning tasks.},
	keywords = {Cross validation, Statistical comparisons of algorithms, Tests for interval-valued data},
	issn = {0022-0000},
	doi = {https://doi.org/10.1016/j.jcss.2013.03.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0022000013000731},
	author = {Jos{\'e} Otero and Luciano S{\'a}nchez and In{\'e}s Couso and Ana Palacios}
}
@article {SANCHEZ20141583,
	title = {Comments on {\textquotedblleft}Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization{\textquotedblright} by Eyke H{\"u}llermeier},
	journal = {International Journal of Approximate Reasoning},
	volume = {55},
	number = {7},
	year = {2014},
	note = {Special issue: Harnessing the information contained in low-quality data sources},
	pages = {1583 - 1587},
	abstract = {The paper by Eyke H{\"u}llermeier introduces a new set of techniques for learning models from imprecise data. The removal of the uncertainty in the training instances through the input{\textendash}output relationship described by the model is also considered. This discussion addresses three points of the paper: extension principle-based models, precedence operators between fuzzy losses and possible connections between data disambiguation and data imputation.},
	keywords = {classification, fuzzy data, Imprecise data, Loss functions, machine learning, Regression},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2014.04.008},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X14000607},
	author = {Luciano S{\'a}nchez}
}
@article {546,
	title = {Comparison and Design of Interpretable Linguistic vs. Scatter FRBSs: GM3M Generalization and New Rule Meaning Index (RMI) for Global Assessment and Local Pseudo-Linguistic Representation},
	journal = {Information Sciences},
	volume = {282},
	year = {2014},
	note = {314031,DPI2012-39381-C02-02,TIN2012-33856,P10-TIC-6858},
	pages = {190{\textendash}213},
	abstract = {This work is devoted to defining more general interpretability indexes to be applied to any scatter or linguistic model implemented by any type of membership functions. They are based on metrics that should take into account the semantic and inference issues: the semantic issue in order to preserve the meaning of the linguistic labels and the inference issue since this can influence the behavior of the rules. On the other hand, these metrics have been designed to be intuitive in order to support the analysis or selection of a final model and to favor a low computational cost within an optimization process. In order to check their usefulness, a multi-objective evolutionary algorithm, simultaneously performing a rule selection and an adjustment of the fuzzy partitions, is guided by the proposed indexes on several benchmark data sets to obtain models with different degrees of accuracy and interpretability. In addition, using these metrics, a local analysis can be carried out between models of a different nature. This local analysis through the model components, gives support to the user to make the best choice from among the models.},
	doi = {10.1016/j.ins.2014.05.023},
	author = {M. Galende and M. J. Gacto and G. Sainz and R. Alcal{\'a}}
}
@conference {321,
	title = {Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms},
	booktitle = {9th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2014)},
	year = {2014},
	note = {TIN2011-28488,TIN2012-33856,P10-TIC-06858,P11-TIC-9704},
	month = {6},
	pages = {110{\textendash}121},
	address = {Salamanca (Spain)},
	abstract = {In the context of multilabel classification, the learning from imbalanced data is getting considerable attention recently. Several algorithms to face this problem have been proposed in the late five years, as well as various measures to assess the imbalance level. Some of the proposed methods are based on resampling techniques, a very well-known approach whose utility in traditional classification has been proven. This paper aims to describe how a specific characteristic of multilabel datasets (MLDs), the level of concurrence among imbalanced labels, could have a great impact in resampling algorithms behavior. Towards this goal, a measure named SCUMBLE, designed to evaluate this concurrence level, is proposed and its usefulness is experimentally tested. As a result, a straightforward guideline on the effectiveness of multilabel resampling algorithms depending on MLDs characteristics can be inferred.},
	isbn = {978-3-319-07616-4},
	doi = {10.1007/978-3-319-07617-1_10},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Cost-Sensitive Learning of Fuzzy Rules for Imbalanced Classification Problems Using FURIA},
	journal = {International Journal of Uncertainty Fuzziness and Knowledge-Based Systems},
	volume = {22},
	year = {2014},
	month = {10},
	pages = {643-675},
	doi = {10.1142/S0218488514500330},
	author = {Palacios, Ana and Trawinski, Krzysztof and O. Cord{\'o}n and S{\'a}nchez, Luciano}
}
@article {Couso2014HarnessingTI,
	title = {Harnessing the information contained in low-quality data sources},
	journal = {Int. J. Approx. Reasoning},
	volume = {55},
	year = {2014},
	pages = {1485-1486},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {article,
	title = {Interval-valued Blind Source Separation Applied to AI-based Prognostic Fault Detection of Aircraft Engines},
	journal = {Journal of Multiple-Valued Logic and Soft Computing},
	volume = {22},
	year = {2014},
	month = {01},
	pages = {151-166},
	author = {Martinez, Alvaro and S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@article {286,
	title = {LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {25},
	number = {10},
	year = {2014},
	note = {TIN2012-33856,TIN2011-28488,TIC-3928,P10-TIC-6858},
	pages = {1842-1854},
	chapter = {1842},
	abstract = {Multilabel classification (MLC) has generated considerable research interest in recent years, as a technique that can be applied to many real-world scenarios. To process them with binary or multiclass classifiers, methods for transforming multilabel data sets (MLDs) have been proposed, as well as adapted algorithms able to work with this type of data sets. However, until now, few studies have addressed the problem of how to deal with MLDs having a large number of labels. This characteristic can be defined as high dimensionality in the label space (output attributes), in contrast to the traditional high dimensionality problem, which is usually focused on the feature space (by means of feature selection) or sample space (by means of instance selection). The purpose of this paper is to analyze dimensionality in the label space in MLDs, and to present a transformation methodology based on the use of association rules to discover label dependencies. These dependencies are used to reduce the label space, to ease the work of any MLC algorithm, and to infer the deleted labels in a final postprocessing stage. The proposed process is validated in an extensive experimentation with several MLDs and classification algorithms, resulting in a statistically significant improvement of performance in some cases, as will be shown.},
	doi = {10.1109/TNNLS.2013.2296501},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {545,
	title = {METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems},
	journal = {Information Sciences},
	volume = {276},
	year = {2014},
	note = {P10-TIC-6858,TIN2011-28488, DPI2012-39381-C02-02,TIN2012-33856},
	pages = {63{\textendash}79},
	abstract = {In this contribution, we propose a two-stage method for Accurate Fuzzy Modeling in High-Dimensional Regression Problems using Approximate Takagi{\textendash}Sugeno{\textendash}Kang Fuzzy Rule-Based Systems. In the first stage, an evolutionary data base learning is performed (involving variables, granularities and slight fuzzy partition displacements) together with an inductive rule base learning within the same process. The second stage is a post-processing process to perform a rule selection and a scatter-based tuning of the membership functions for further refinement of the learned solutions. Moreover, the second stage incorporates an efficient Kalman filter to learn the coefficients of the consequent polynomial function in the Takagi{\textendash}Sugeno{\textendash}Kang rules. Both stages include mechanisms that significantly improve the accuracy of the model and ensure a fast convergence in high-dimensional and large-scale regression datasets. We tested our approach on 28 real-world datasets with different numbers of variables and instances. Five well-known methods have been executed as references. We compared the different approaches by applying non-parametric statistical tests for pair-wise and multiple comparisons. The results confirm the effectiveness of the proposed method, showing better results in accuracy within a reasonable computing time.},
	doi = {10.1016/j.ins.2014.02.047},
	author = {M. J. Gacto and M. Galende and R. Alcal{\'a} and F. Herrera}
}
@conference {320,
	title = {MLeNN: A First Approach to Heuristic Multilabel Undersampling},
	booktitle = {15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014},
	year = {2014},
	note = {TIN2011-28488,TIN2012-33856,P10-TIC-06858,P11-TIC-9704},
	month = {9},
	pages = {1-9},
	address = {Salamanca (Spain)},
	abstract = {Learning from imbalanced multilabel data is a challenging task that has attracted considerable attention lately. Some resampling algorithms used in traditional classification, such as random undersampling and random oversampling, have been already adapted in order to work with multilabel datasets. In this paper MLeNN (MultiLabel edited Nearest Neighbor), a heuristic multilabel undersampling algorithm based on the well-known Wilson{\textquoteright}s Edited Nearest Neighbor Rule, is proposed. The samples to be removed are heuristically selected, instead of randomly picked. The ability of MLeNN to improve classification results is experimentally tested, and its performance against multilabel random undersampling is analyzed. As will be shown, MLeNN is a competitive multilabel undersampling alternative, able to enhance significantly classification results.},
	isbn = {978-3-319-10839-1},
	doi = {10.1007/978-3-319-10840-7_1},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/978-3-319-07617-1_30,
	title = {Mutual Information-Based Feature Selection in Fuzzy Databases Applied to Searching for the Best Code Metrics in Automatic Grading},
	booktitle = {Hybrid Artificial Intelligence Systems},
	year = {2014},
	pages = {330{\textendash}341},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Massive open online courses have a large impact in developing countries, helping to improve education in poor regions. However, instructors cannot review open-ended work from students as they do in smaller class settings. In the context of computer science courses where students{\textquoteright} code is reviewed, there are some methods that use code metrics for automatically providing the student with a qualification. Notwithstanding this, a high number of incomplete and conflicting sources of information must be combined in the prediction process, and it may happen that there are too many variables involved to make any meaningful predictions. In this work a new method is proposed for sorting a set of metrics by their relevance in the prediction of student qualifications that can cope with incomplete and imprecise results. Measurements taken on variable-sized sets of assignments are aggregated into fuzzy values, and a fuzzy random variable-based definition of mutual information is used to build a partial ranking of metrics according to their predictive power. The most relevant metrics are fed to a genetic fuzzy system that models the dependence between the fuzzy code metrics and the qualifications of the corresponding students. A set comprising 800 source code files, collected in classroom Computer Science lectures taught between 2013 and 2014, was used for validating the hypotheses of this research, and it was found that the new ranking method significantly improves the predictive capability of the models.},
	isbn = {978-3-319-07617-1},
	author = {Otero, Jos{\'e} and Su{\'a}rez, Rosario and S{\'a}nchez, Luciano},
	editor = {Polycarpou, Marios and de Carvalho, Andr{\'e} C. P. L. F. and Pan, Jeng-Shyang and Wo{\'z}niak, Micha{\l} and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@article {Cgdh14,
	title = {Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {4},
	number = {2},
	year = {2014},
	note = {TIN2012-33856,TIN2010-15055,TIN2011-28488,TIC-3928,P11-TIC-7765,P10-TIC-6858},
	pages = {87-103},
	doi = {10.1002/widm.1118},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {302,
	title = {Propuesta de una asignatura de Dise{\~n}o de Servidores para la especialidad de Tecnolog{\'\i}as de Informaci{\'o}n},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {4},
	year = {2014},
	note = {-},
	pages = {15{\textendash}24},
	abstract = {This paper presents the subject Design and Deployment of Servers belonging to the Information Technologies of the Computer Science Engineering at the University of Ja{\'e}n. The objective of this subject is to provide the training established in the different competences related to the development and deployment of hardware infrastructures that supports the currently information systems. These information systems have characteristics such as ubiquitous access, high computational costs or high availability, among others. Thus, the subject addresses concepts from design of systems, monitoring, benchmarking or evaluation, to high availability, scalability or load balancing.},
	issn = {2173-8688},
	author = {A.J. Rivera-Rivas and Espinilla, Macarena and A. Fern{\'a}ndez and Santamar{\'\i}a L{\'o}pez, Jos{\'e} and Francisco Charte}
}
@conference {10.1007/978-3-319-08644-6_31,
	title = {Selecting the Most Informative Inputs in Modelling Problems with Vague Data Applied to the Search of Informative Code Metrics for Continuous Assessment in Computer Science Online Courses},
	booktitle = {Rough Sets and Current Trends in Computing},
	year = {2014},
	pages = {299{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Sorting a set of inputs for relevance in modeling problems may be ambiguous if the data is vague. A general extension procedure is proposed in this paper that allows applying different deterministic or random feature selection algorithms to fuzzy data. This extension is based on a model of the relevance of a feature as a possibility distribution. The possibilistic relevances are ordered with the help of a fuzzy ranking. A practical problem where the most informative software metrics are searched for in an automatic grading problem is solved with this technique.},
	isbn = {978-3-319-08644-6},
	author = {Otero, Jos{\'e} and Del Rosario Su{\'a}rez, Maria and Palacios, Ana and Couso, In{\'e}s and S{\'a}nchez, Luciano},
	editor = {Cornelis, Chris and Kryszkiewicz, Marzena and {\'S}lȩzak, Dominik and Ruiz, Ernestina Menasalvas and Bello, Rafael and Shang, Lin}
}
@article {595,
	title = {Short, medium and long term forecasting of time series using the L-Co-R algorithm},
	journal = {Neurocomputing},
	volume = {128},
	year = {2014},
	note = {TIC-3928,TIN2012-33856,TIN2011-28627-C02},
	pages = {433-446},
	issn = {0925-2312},
	doi = {10.1016/j.neucom.2013.08.023},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M.G. Arenas and M. J. del Jesus}
}
@conference {inproceedings,
	title = {Supervising classrooms comprising children with dyslexia and other learning problems with graphical exploratory analysis for fuzzy data: Presentation of the software tool and case study},
	booktitle = {IEEE International Conference on Fuzzy Systems},
	year = {2014},
	month = {07},
	pages = {2133-2140},
	doi = {10.1109/FUZZ-IEEE.2014.6891710},
	author = {Palacios, Ana and S{\'a}nchez, Luciano}
}
@article {COUSO2014114,
	title = {The notion of roughness of a fuzzy set},
	journal = {Fuzzy Sets and Systems},
	volume = {249},
	year = {2014},
	note = {Theme: Automata Theory and Algebra},
	pages = {114 - 127},
	abstract = {The roughness of a set (according to the notion introduced by Pawlak in 1991) can be regarded as the MZ-distance between its upper and the lower approximations. With this idea in mind, we have generalized Pawlak{\textquoteright}s definition, by replacing the MZ-distance by a general {\textquotedblleft}distance{\textquotedblright} measure. We also generalize the notion of roughness of fuzzy sets introduced by Huyhn and Nakamori in 2005.},
	keywords = {Dissimilarity measure, Divergence measure, fuzzy set, Rough set, Roughness},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2014.02.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011414000657},
	author = {In{\'e}s Couso and Laura Garrido and Luciano S{\'a}nchez}
}
@article {543,
	title = {Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets},
	journal = {Applied Soft Computing},
	volume = {25},
	year = {2014},
	note = {TIN2012-33856},
	pages = {26-39},
	doi = {10.1016/j.asoc.2014.09.011},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and C. J. Carmona and M. J. del Jesus}
}
@conference {6706973,
	title = {A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments},
	booktitle = {The 2013 International Joint Conference on Neural Networks (IJCNN)},
	year = {2013},
	month = {Aug},
	pages = {1-8},
	abstract = {Many real applications are composed of data sets where the distribution of the classes is significantly different. These data sets are commonly known as imbalanced data sets. Proposed approaches that address this problem can be categorized into two types: data-based, which resample problem data in a preprocessing phase and algorithm-based which modify or create new methods to address the imbalance problem. In this paper, CO2 RBFN a cooperative-competitive design method for Radial Basis Function Networks that has previously demonstrated a good behaviour tackling imbalanced data sets, is tested using two different training weights algorithms, local and global, in order to gain knowledge about this problem. As conclusions we can outline that a more global optimizer training algorithm obtains worse results.},
	keywords = {Accuracy, Algorithm design and analysis, algorithm-based approach, CO2RBFN, cooperative-competitive design method for radial basis function networks, data-based approach, global optimization weights methods, global optimizer training algorithm, imbalanced data sets, learning (artificial intelligence), Least squares approximations, local optimization weights methods, Neurons, optimisation, radial basis function networks, Sociology, Training, training weights algorithms},
	issn = {2161-4407},
	doi = {10.1109/IJCNN.2013.6706973},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. del Jesus and Francisco Mart{\'\i}nez}
}
@conference {322,
	title = {A First Approach to Deal with Imbalance in Multi-label Datasets},
	booktitle = {8th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2013)},
	year = {2013},
	note = {TIN2012-33856,TIN2011-28488,TIC-3928,P10-TIC-6858},
	month = {9},
	pages = {150-160},
	address = {Salamanca (Spain)},
	abstract = {The process of learning from imbalanced datasets has been deeply studied for binary and multi-class classification. This problem also affects to multi-label datasets. Actually, the imbalance level in multi-label datasets uses to be much larger than in binary or multi-class datasets. Notwithstanding, the proposals on how to measure and deal with imbalanced datasets in multi-label classification are scarce. In this paper, we introduce two measures aimed to obtain information about the imbalance level in multi-label datasets. Furthermore, two preprocessing methods designed to reduce the imbalance level in multi-label datasets are proposed, and their effectiveness is validated experimentally. Finally, an analysis for determining when these methods have to be applied depending on the dataset characteristics is provided.},
	isbn = {978-3-642-40845-8},
	doi = {10.1007/978-3-642-40846-5_16},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {585,
	title = {A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-based Multiclassifiers Revista: IEEE Transactions on Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {21},
	number = {5},
	year = {2013},
	month = {01},
	pages = {950-965},
	issn = {1063-6706},
	doi = {10.1109/TFUZZ.2012.2236844},
	author = {O. Cord{\'o}n and S{\'a}nchez, Luciano and Arnaud Quirin and Trawinski, Krzysztof}
}
@article {LOPEZ201385,
	title = {A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets},
	journal = {Knowledge-Based Systems},
	volume = {38},
	year = {2013},
	note = {Special Issue on "Advances in Fuzzy Knowledge Systems: Theory and Application"},
	pages = {85 - 104},
	abstract = {Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out. Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behavior in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model. An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.},
	keywords = {Borderline examples, Fuzzy rule based classification systems, Genetic rule selection, Hierarchical fuzzy partitions, Imbalanced data-sets, Tuning},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2012.08.025},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705112002596},
	author = {Victoria L{\'o}pez and Alberto Fernandez and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {A methodology for exploiting the tolerance for imprecision in genetic fuzzy systems and its application to characterization of rotorblade leading edge materials},
	journal = {Mechanical Systems and Signal Processing},
	volume = {37},
	year = {2013},
	month = {06},
	pages = {76-91},
	doi = {10.1016/j.ymssp.2012.02.009},
	author = {S{\'a}nchez, Luciano and Couso, In{\'e}s and Palacios, Ana and Palacios, Jos{\'e}}
}
@conference {10.1007/978-3-642-32922-7_45,
	title = {A Performance Study of Concentrating Photovoltaic Modules Using Neural Networks: An Application with CO2RBFN},
	booktitle = {Soft Computing Models in Industrial and Environmental Applications},
	year = {2013},
	pages = {439{\textendash}448},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Concentrating Photovoltaic (CPV) technology attempts to optimize the efficiency of solar energy production systems and models for determining the exact module performance are needed. In this paper, a CPV module is studied by means of atmospheric conditions obtained using an automatic test and measuring system. CO2RBFN, a cooperative-competitive algorithm for the design of radial basis neural networks, is adapted and applied to these data obtaining a model with a good level of accuracy on test data, improving the results obtained by other methods considered in the experimental comparison. These initial results are promising and the obtained model could be used to work out the maximum power at the CPV reporting conditions and to analyze the performance of the module under any conditions and at any moment.},
	isbn = {978-3-642-32922-7},
	author = {A.J. Rivera-Rivas and B. Garc{\'\i}a-Domingo and M. J. del Jesus and J. Aguilera},
	editor = {Sn{\'a}{\v s}el, V{\'a}clav and Abraham, Ajith and Corchado, Emilio S.}
}
@conference {323,
	title = {Alternative OVA Proposals for Cooperative Competitive RBFN Design in Classification Tasks},
	booktitle = {12th International Work-Conference on Artificial Neural Networks (IWANN 2013)},
	year = {2013},
	note = {TIN2012-33856,TIC-3928},
	pages = {331-338},
	address = {Tenerife (Spain)},
	abstract = {In the Machine Learning field when the multi-class classification problem is addressed, one possibility is to transform the data set in binary data sets using techniques such as One-Versus-All. One classifier must be trained for each binary data set and their outputs combined in order to obtain the final predicted class. The determination of the strategy used to combine the output of the binary classifiers is an interesting research area. In this paper different OVA strategies are developed and tested using as base classifier a cooperative-competitive RBFN design algorithm, CO2RBFN. One advantage of the obtained models is that they obtain as output for a given class a continuous value proportional to its level of confidence. Concretely three OVA strategies have been tested: the classical one, one based on the difference among outputs and another one based in a voting scheme, that has obtained the best results.},
	isbn = {978-3-642-38678-7},
	doi = {10.1007/978-3-642-38679-4_32},
	author = {Francisco Charte and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus}
}
@conference {Cggda13b,
	title = {An evolutionary fuzzy system for the detection of exceptions in subgroup discovery},
	booktitle = {International Fuzzy Systems Applications World Congress (IFSA)},
	year = {2013},
	note = {TIN2012-33856,ENE2009-08302,TIC-3928,TEP-5045M},
	month = {July},
	pages = {74-79},
	address = {Edmonton (Canada)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and B. Garc{\'\i}a-Domingo and M. J. del Jesus and J. Aguilera}
}
@conference {Ccsd13b,
	title = {An{\'a}lisis del virus de la gripe A mediante descubrimientos de subgrupos difusos},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos (TAMIDA)},
	year = {2013},
	note = {TIN2012-33856,TIC-3928},
	month = {September},
	pages = {1313-1322},
	address = {Madrid (Spain)},
	author = {C. J. Carmona and C. Chrysostomou and H. Seker and M. J. del Jesus}
}
@article {FERNANDEZ201397,
	title = {Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches},
	journal = {Knowledge-Based Systems},
	volume = {42},
	year = {2013},
	pages = {97 - 110},
	abstract = {The imbalanced class problem is related to the real-world application of classification in engineering. It is characterised by a very different distribution of examples among the classes. The condition of multiple imbalanced classes is more restrictive when the aim of the final system is to obtain the most accurate precision for each of the concepts of the problem. The goal of this work is to provide a thorough experimental analysis that will allow us to determine the behaviour of the different approaches proposed in the specialised literature. First, we will make use of binarization schemes, i.e., one versus one and one versus all, in order to apply the standard approaches to solving binary class imbalanced problems. Second, we will apply several ad hoc procedures which have been designed for the scenario of imbalanced data-sets with multiple classes. This experimental study will include several well-known algorithms from the literature such as decision trees, support vector machines and instance-based learning, with the intention of obtaining global conclusions from different classification paradigms. The extracted findings will be supported by a statistical comparative analysis using more than 20 data-sets from the KEEL repository.},
	keywords = {Cost-sensitive learning, Imbalanced data-sets, Multi-classification, Pairwise learning, Preprocessing},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2013.01.018},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705113000300},
	author = {Alberto Fernandez and Victoria L{\'o}pez and Mikel Galar and M. J. del Jesus and F. Herrera and ELSEVIER SCIENCE BV}
}
@article {Cano13,
	title = {Analysis of data complexity measures for classification},
	journal = {Expert Systems with Applications},
	volume = {40},
	number = {12},
	year = {2013},
	pages = {4820{\textendash}4831},
	doi = {10.1016/j.eswa.2013.02.025},
	url = {http://dx.doi.org/10.1016/j.eswa.2013.02.025},
	author = {J. R. Cano}
}
@inbook {Cdg13,
	title = {Applying Subgroup Discovery Based on Evolutionary Fuzzy Systems for Web Usage Mining in E-Commerce: A Case Study on OrOliveSur.com},
	booktitle = {Foundations and Applications of Intelligent Systems},
	year = {2013},
	note = {TIN2008-06681-C06-02,TIC-3928,UJA2010/13/07},
	pages = {591-602},
	isbn = {978-3-642-37828-7},
	author = {C. J. Carmona and M. J. del Jesus},
	editor = {F. Sun and T. Li and H. Li}
}
@article {article,
	title = {Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks},
	journal = {Expert Systems with Applications},
	volume = {40},
	number = {05},
	year = {2013},
	month = {04},
	pages = {1599{\textendash}1608},
	issn = {0957-4174},
	doi = {10.1016/j.eswa.2012.09.016},
	author = {Rivera Rivas, Antonio and B. Garc{\'\i}a-Domingo and M. J. del Jesus and J. Aguilera}
}
@conference {6622418,
	title = {CI-LQD: A software tool for modeling and decision making with Low Quality Data},
	booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	month = {July},
	pages = {1-8},
	abstract = {The software tool CI-LQD (Computational Intelligence for Low Quality Data) is introduced in this paper. CI-LQD is an ongoing project that includes a lightweight open source software that has been designed with scientific and teaching purposes in mind. The main usefulness of the software is to automate the calculations involved in the statistical comparisons of different algorithms, with both numerical and graphical techniques, when the available information is interval-valued, fuzzy, incomplete or otherwise vague. A growing catalog of evolutionary algorithms for learning classifiers, models and association rules, along with their corresponding data conditioning and preprocessing techniques, is included. A demonstrative example of the tool is described that illustrates the capabilities of the software.},
	keywords = {Algorithm design and analysis, Artificial intelligence, Association rules, CI-LQD, Computational intelligence, data conditioning, data mining, data preprocessing technique, Databases, Decision making, evolutionary algorithm, Evolutionary algorithms, evolutionary computation, graphical technique, learning classifier, lightweight open source software, Low Quality Data, Probability distribution, Software algorithms, software tool, software tools, statistical analysis, statistical comparison},
	issn = {1098-7584},
	doi = {10.1109/FUZZ-IEEE.2013.6622418},
	author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@article {623,
	title = {Descubrimiento de subgrupos aplicado al portal de comercio electr{\'o}nico: OrOliveSur.com},
	year = {2013},
	pages = {1-16},
	institution = {Departamento de Inform{\'a}tica, Universidad de Ja{\'e}n},
	address = {Ja{\'e}n, Espa{\~n}a},
	author = {C. J. Carmona and M. J. del Jesus and S. Garc{\'\i}a}
}
@conference {6622420,
	title = {Engine Health Monitoring for engine fleets using fuzzy radviz},
	booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	month = {July},
	pages = {1-8},
	abstract = {A new algorithm for assessment of Engine Health Monitoring (EHM) data in aircraft is proposed. The diagnostic tool quantifies step changes, shifts and trends in EHM data by means of a transformation that aggregates concurrent readings of EHM data into a single fuzzy state. A Genetic Fuzzy System is used to detect the occurance of a specific trend of interest in the sequence of states. The activation of the rules is represented in a 2D map by means of an extension of the Radviz visualization algorithm to fuzzy data.},
	keywords = {2D map, aerospace engineering, aerospace engines, aircraft, Bandwidth, condition monitoring, data visualisation, diagnostic tool, EHM data, engine fleets, engine health monitoring, Engines, fault diagnosis, fuzzy data, fuzzy Radviz, fuzzy set theory, genetic algorithms, genetic fuzzy system, Genetic Fuzzy Systems, Low Quality Data, Maintenance engineering, Market research, mechanical engineering computing, Monitoring, Radviz visualization algorithm, rule activation, single fuzzy state, states sequence, Temperature measurement, Turbines},
	issn = {1098-7584},
	doi = {10.1109/FUZZ-IEEE.2013.6622420},
	author = {A. Mart{\'\i}nez and L. S{\'a}nchez and I. Couso}
}
@article {Ccsd13,
	title = {Fuzzy Rules for Describing Subgroups from Influenza A Virus Using a Multi-objective Evolutionary Algorithm},
	journal = {Applied Soft Computing},
	volume = {13},
	year = {2013},
	note = {TIN2012-33856,TIC-3928},
	pages = {3439-3448},
	doi = {10.1016/j.asoc.2013.04.011},
	author = {C. J. Carmona and C. Chrysostomou and H. Seker and M. J. del Jesus}
}
@article {Cggda13,
	title = {MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology},
	journal = {Knowledge-Based Systems},
	volume = {54},
	year = {2013},
	note = {IEE-07-762,TIN2012-33856,ENE2009-08302,TIC-3928,TEP-5045M},
	pages = {73-85},
	doi = {10.1016/j.knosys.2013.08.001},
	author = {C. J. Carmona and P. Gonz{\'a}lez and B. Garc{\'\i}a-Domingo and M. J. del Jesus and J. Aguilera}
}
@article {TRAWINSKI20133,
	title = {Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?},
	journal = {Knowledge-Based Systems},
	volume = {54},
	year = {2013},
	pages = {3 - 21},
	abstract = {Recently we proposed the use of the Random Linear Oracles classical classifier ensemble (CE) design methodology in a fuzzy environment. It derived fuzzy rule-based CEs obtaining an outstanding performance. Random Oracles introduce an additional diversity into the base classifiers improving the accuracy of the entire CE. Meanwhile, the overproduce-and-choose strategy leads to a good accuracy-complexity trade-off. It is based on the generation of a large number of component classifiers and a subsequent selection of the best cooperating subset of them. The current contribution has a twofold aim: (1) Introduce a new Random Oracles approach into the fuzzy rule-based CEs design; (2) Incorporate an evolutionary multi-objective overproduce-and-choose strategy to our approach analyzing the influence of this additional diversity in the final CE performance (focusing on the accuracy). To do so, firstly, we incorporate the two Random Oracle variants into the fuzzy rule-based CE framework. Then, we use NSGA-II to provide a specific component classifier selection driven by three different criteria. Exhaustive experiments are carried out over 29 UCI and KEEL datasets with high complexity (considering both the number of attributes as well as the number of examples) showing the good performance of the proposed approach.},
	keywords = {Bagging, Diversity measures, Evolutionary multiobjective optimization, Fuzzy rule-based classifier ensembles, Genetic classifier selection, High complexity datasets, NSGA-II, Random oracles},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2013.08.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705113002360},
	author = {Krzysztof Trawi{\'n}ski and O. Cord{\'o}n and Arnaud Quirin and Luciano S{\'a}nchez}
}
@conference {544,
	title = {Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	note = {TIN2011-28488, DPI2009-14410-C02-02,P10-TIC-6858},
	month = {07/2013},
	pages = {1-7},
	abstract = {Abstract{\textemdash}This paper addresses the challenging problem of fuzzy modeling in high-dimensional and large scale regression datasets. To this end, we propose a scalable two-stage method for obtaining accurate fuzzy models in high-dimensional regression problems using approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, we propose an effective Multi-Objective Evolutionary Algorithm, based on an embedded genetic Data Base learning (involved variables, granularities and a slight lateral displacement of fuzzy partitions) together with an inductive rule base learning within the same process. The second stage is a post-processing process based on a second MOEA to perform a rule selection and a fine scatter-based tuning of the Membership Functions. Moreover, it incorporates an efficient Kalman filter to estimate the coefficients of the consequent polynomial functions in the Takagi-Sugeno-Kang rules. In both stages, we include mechanisms in order to significantly improve the accuracy of the model and to ensure a fast convergence in high-dimensional regression problems. The proposed method is compared to the classical ANFIS method and to a well-known evolutionary learning algorithm for obtaining accurate TSK systems in 8 datasets with different sizes and dimensions, obtaining better results.},
	author = {M. J. Gacto and M. Galende and R. Alcal{\'a} and F. Herrera}
}
@conference {593,
	title = {Predicci{\'o}n de tr{\'a}fico mediante co-evoluci{\'o}n de Redes Neuronales de Funciones de Base Radial y selecci{\'o}n de variables de entrada},
	booktitle = {Congreso Espa{\~n}ol de Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2013},
	note = {TIC-3903,P08-TIC-3928,CEI2013-P-14,TIN2011-28627-C04-02,SIPEsCa del Programa Operativo FEDER de Andaluc{{\'\i}a 2007-2013},
	pages = {782-791},
	address = {Madrid (Spain)},
	author = {V. M. Rivas and E. Parras-Guti{\'e}rrez and M.G. Arenas and P.A. Castillo and P. Garcia-Sanchez and Juan J. Merelo and P. Garcia-Fernandez}
}
@article {Couso2013SimilarityAD,
	title = {Similarity and dissimilarity measures between fuzzy sets: A formal relational study},
	journal = {Inf. Sci.},
	volume = {229},
	year = {2013},
	pages = {122-141},
	author = {In{\'e}s Couso and Laura Garrido and Luciano S{\'a}nchez}
}
@conference {594,
	title = {The L-Co-R Co-evolutionary Algorithm: A Comparative Analysis in Medium-term Time-series Forecasting Problems},
	booktitle = {International Joint Conference on Computational Intelligence (IJCCI)},
	year = {2013},
	note = {TIC-3928,TIC-03903,TIN2012-33856,TIN2011-28627-C04-02,SIPEsCa Programa Operativo FEDER de Andaluc{{\'\i}a 2007- 2013},
	pages = {144-151},
	address = {Vilamoura, Algarve (Portugal)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and Juan J. Merelo}
}
@conference {440,
	title = {Un sistema difuso evolutivo para la detecci{\'o}n de excepciones en descubrimiento de subgrupos},
	booktitle = {XV Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial},
	year = {2013},
	note = {TIN2012-33856,TIC-3928},
	month = {September},
	pages = {1220-1229},
	address = {Madrid (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {simidat193,
	title = {A Multi-Objective Evolutionary Algorithm for an Effective Tuning of Fuzzy Logic Controllers in Heating, Ventilating and Air Conditioning Systems},
	journal = {Applied Intelligence},
	volume = {36},
	number = {2},
	year = {2012},
	pages = {330-347},
	doi = {10.1007/s10489-010-0264-x},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {Clgd12b,
	title = {A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	month = {June},
	pages = {1-7},
	address = {Brisbane (Australia)},
	author = {C. J. Carmona and J. Luengo and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {simidat225,
	title = {A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms},
	booktitle = {1st International Conference on Pattern Recognition Applications and Methods (ICPRAM)},
	year = {2012},
	note = {TIN2011-28488,TIC-6858},
	month = {February},
	pages = {211-216},
	address = {Villamoura - (Portugal)},
	author = {S. Garc{\'\i}a and V. L{\'o}pez and J. Luengo and C. J. Carmona and F. Herrera}
}
@article {simidat232,
	title = {A Review on Ensembles for Class Imbalance Problem: Bagging, Boosting and Hybrid Based Approaches},
	journal = {IEEE Transactions on System, Man and Cybernetics - Part C: Applications and Reviews},
	volume = {42},
	number = {4},
	year = {2012},
	pages = {463-484},
	doi = {10.1109/TSMCC.2011.2161285},
	author = {M. Galar and A. Fern{\'a}ndez and E. Barrenechea and H. Bustince and F. Herrera}
}
@conference {simidat224,
	title = {Ajuste gen{\'e}tico lateral de las etiquetas ling{\"u}{\'\i}sticas en descubrimiento de subgrupos},
	booktitle = {XVI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	month = {February},
	pages = {271-276},
	address = {Valladolid (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. Gacto and M. J. del Jesus}
}
@article {Clgd12,
	title = {An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery},
	journal = {Expert Systems with Applications},
	volume = {39},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	pages = {11404-11412},
	doi = {10.1016/j.eswa.2012.04.029},
	author = {C. J. Carmona and J. Luengo and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {624,
	title = {Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems},
	year = {2012},
	pages = {1-28},
	institution = {De Montfort University},
	address = {Leicester, United Kingdom},
	author = {C. J. Carmona and D. Elizondo}
}
@article {simidat231,
	title = {Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics},
	journal = {Expert Systems with Applications},
	volume = {39},
	number = {7},
	year = {2012},
	pages = {6585-6608},
	doi = {10.1016/j.eswa.2011.12.043},
	author = {V. L{\'o}pez and A. Fern{\'a}ndez and J.G. Moreno-Torres and F. Herrera}
}
@conference {simidat223,
	title = {Aprendizaje Evolutivo De Sistemas Aproximativos De Tipo TSK Para Problemas De Alta Dimensionalidad},
	booktitle = {XVI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2012},
	month = {February},
	pages = {295-300},
	address = {Valladolid (Spain)},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat243,
	title = {Automatic Laser Pointer Detection Algorithm for Environment Control Device Systems Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based Systems},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {5},
	number = {2},
	year = {2012},
	pages = {368-386},
	doi = {10.1080/18756891.2012.685327},
	author = {F. Ch{\'a}vez and A. Fern{\'a}ndez and M. J. Gacto and R. Alcal{\'a}}
}
@article {simidat241,
	title = {Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm},
	journal = {Soft Computing},
	volume = {16},
	number = {6},
	year = {2012},
	pages = {919-942},
	author = {E. Parras-Guti{\'e}rrez and M. Garc{\'\i}a-Arenas and V. M. Rivas and M. J. del Jesus}
}
@article {doi:10.1142/S0218488512400156,
	title = {COMBINING ADABOOST WITH PREPROCESSING ALGORITHMS FOR EXTRACTING FUZZY RULES FROM LOW QUALITY DATA IN POSSIBLY IMBALANCED PROBLEMS},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {20},
	number = {supp02},
	year = {2012},
	pages = {51-71},
	abstract = {An extension of the Adaboost algorithm for obtaining fuzzy rule-based systems from low quality data is combined with preprocessing algorithms for equalizing imbalanced datasets. With the help of synthetic and real-world problems, it is shown that the performance of the Adaboost algorithm is degraded in presence of a moderate uncertainty in either the input or the output values. It is also established that a preprocessing stage improves the accuracy of the classifier in a wide range of binary classification problems, including those whose imbalance ratio is uncertain.},
	doi = {10.1142/S0218488512400156},
	url = {https://doi.org/10.1142/S0218488512400156},
	author = {Palacios, Ana and S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@conference {inproceedings,
	title = {Cost Sensitive and Preprocessing for Classification with Imbalanced Data-sets: Similar Behaviour and Potential Hybridizations},
	booktitle = {ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods},
	volume = {2},
	year = {2012},
	month = {02},
	author = {L{\'o}pez, Victoria and Fern{\'a}ndez, Alberto and M. J. del Jesus and F. Herrera}
}
@article {PALACIOS201210212,
	title = {Eliciting a human understandable model of ice adhesion strength for rotor blade leading edge materials from uncertain experimental data},
	journal = {Expert Systems with Applications},
	volume = {39},
	number = {11},
	year = {2012},
	pages = {10212 - 10225},
	abstract = {The published ice adhesion performance data of novel {\textquotedblleft}ice-phobic{\textquotedblright} coatings varies significantly, and there are not reliable models of the properties of the different coatings that help the designer to choose the most appropriate material. In this paper it is proposed not to use analytical models but to learn instead a rule-based system from experimental data. The presented methodology increases the level of post-processing interpretation accuracy of experimental data obtained during the evaluation of ice-phobic materials for rotorcraft applications. Key to the success of this model is a possibilistic representation of the uncertainty in the data, combined with a fuzzy fitness-based genetic algorithm that is capable to elicit a suitable set of rules on the basis of incomplete and imprecise information.},
	keywords = {fuzzy rule-based classifiers, Genetic Fuzzy Systems, Ice-phobic materials, Isotropic materials, Shear adhesion strength, vague data},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2012.02.155},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417412004186},
	author = {Ana M. Palacios and Jos{\'e} L. Palacios and Luciano S{\'a}nchez}
}
@article {Gdtch12,
	title = {Evolutionary-Based Selection of Generalized Instances for Imbalanced Classification},
	journal = {Knowledge-Based Systems},
	volume = {25},
	number = {1},
	year = {2012},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02},
	pages = {3-12},
	doi = {10.1016/j.knosys.2011.01.012},
	author = {S. Garc{\'\i}a and J. Derrac and I. Triguero and C. J. Carmona and F. Herrera}
}
@article {simidat233,
	title = {Feature Selection and Granularity Learning in Genetic Fuzzy Rule-Based Classication Systems for Highly Imbalanced Data-Sets},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {20},
	number = {3},
	year = {2012},
	pages = {369-397},
	doi = {S0218488512500195},
	author = {P. Villar and A. Fern{\'a}ndez and R. Carrasco and F. Herrera}
}
@article {Cggd12,
	title = {Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {5},
	number = {2},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	pages = {355-367},
	doi = {10.1080/18756891.2012.685323},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. Gacto and M. J. del Jesus}
}
@article {simidat234,
	title = {IIVFDT: Ignorance Functions based Interval-Valued Fuzzy Decision Tree with Genetic Tuning. International Journal of Uncertainty},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	year = {2012},
	author = {J. Sanz and A. Fern{\'a}ndez and H. Bustince}
}
@conference {324,
	title = {Improving Multi-label Classifiers via Label Reduction with Association Rules},
	booktitle = {7th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2012)},
	year = {2012},
	note = {TIN2008-06681-C06-02,TIC-3928},
	month = {9},
	pages = {188{\textendash}199},
	address = {Salamanca (Spain)},
	abstract = {Multi-label classification is a generalization of well known problems, such as binary or multi-class classification, in a way that each processed instance is associated not with a class (label) but with a subset of these. In recent years different techniques have appeared which, through the transformation of the data or the adaptation of classic algorithms, aim to provide a solution to this relatively recent type of classification problem. This paper presents a new transformation technique for multi-label classification based on the use of association rules aimed at the reduction of the label space to deal with this problem.},
	isbn = {978-3-642-28930-9},
	doi = {10.1007/978-3-642-28931-6_18},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {simidat242,
	title = {Mining Fuzzy Association Rules from Low Quality Data},
	journal = {Soft Computing},
	volume = {16},
	number = {5},
	year = {2012},
	pages = {883-901},
	doi = {10.1007/s00500-011-0775-3},
	author = {A. Palacios and M. J. Gacto and J. Alcal{\'a}-Fdez}
}
@article {Cano12,
	title = {Predictive-collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support},
	journal = {Expert Systems with Applications},
	volume = {39},
	number = {4},
	year = {2012},
	pages = {4044{\textendash}4048},
	doi = {10.1016/j.eswa.2011.09.098},
	author = {J. R. Cano}
}
@inbook {simidat230,
	title = {PRESETEMP: Predicci{\'o}n de Series Temporales mediante t{\'e}cnicas de Miner{\'\i}a de Datos},
	booktitle = {Proyectos de Investigaci{\'o}n 2009-10},
	year = {2012},
	isbn = {978-84-8439-642-0},
	author = {V. M. Rivas and E. Parras-Guti{\'e}rrez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. Olmo and M.G. Arenas and A. Fernandez-Montoya and M. Cillero},
	editor = {Eulogio Castro Galiano}
}
@article {GarciaDCH12,
	title = {Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study},
	journal = {IEEE Transactions Pattern Analysis and Machiche Intelligence},
	volume = {34},
	number = {3},
	year = {2012},
	pages = {417{\textendash}435},
	doi = {10.1109/TPAMI.2011.142},
	author = {S. Garc{\'\i}a and J. Derrac and J. R. Cano and F. Herrera}
}
@article {simidat59,
	title = {Replacement Strategies to Preserve Useful Diversity in Steady-State Genetic Algorithms},
	journal = {Information Sciences},
	year = {2012},
	author = {M. Lozano and F. Herrera and J. R. Cano}
}
@conference {Cstbdg12,
	title = {Subgroup Discovery Applied to the e-Commerce Website OrOliveSur.com},
	booktitle = {14th International Conference on Enterprise Information Systems (ICEIS)},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928,UJA2010/13/07},
	month = {July},
	pages = {239-244},
	address = {Wroclaw (Poland)},
	author = {C. J. Carmona and S. Ram{\'\i}rez-Gallego and F. Torres and E. Bernal and M. J. del Jesus}
}
@conference {inproceedings,
	title = {Un sistema de clasificaci{\'o}n basado en reglas difusas jer{\'a}rquico con programaci{\'o}n gen{\'e}tica para problemas de clasificaci{\'o}n altamente no balanceados},
	year = {2012},
	month = {02},
	author = {L{\'o}pez, Victoria and Fern{\'a}ndez, Alberto and M. J. del Jesus and F. Herrera}
}
@conference {662,
	title = {Universol Project. Final Results},
	booktitle = {26 th European Photovoltaic Solar Energy Conference},
	year = {2012},
	month = {01},
	address = {Hamburgo, Alemania},
	author = {M. J. del Jesus and Casa-Higueras, Juan De La}
}
@article {Cstbdg12,
	title = {Web usage mining to improve the design of an e-commerce website: OrOliveSur.com},
	journal = {Expert Systems with Applications},
	volume = {39},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928,UJA2010/13/07},
	pages = {11243-11249},
	doi = {10.1016/j.eswa.2012.03.046},
	author = {C. J. Carmona and S. Ram{\'\i}rez-Gallego and F. Torres and E. Bernal and M. J. del Jesus}
}
@conference {simidat217,
	title = {A double axis classi},
	booktitle = {9th International Workshop on Fuzzy Logic and Applications (WILF)},
	year = {2011},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {10.1007/978-3-642-23713-3_13,
	title = {A Double Axis Classification of Interpretability Measures for Linguistic Fuzzy Rule-Based Systems},
	booktitle = {Fuzzy Logic and Applications},
	year = {2011},
	pages = {99{\textendash}106},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we present a simple classification of the papers devoted to interpretability of Linguistic Fuzzy Rule-Based Systems attending to the type of interpretability measures and the part of the system for which they are applied, i.e., a double axis classification. A taxonomy considering this double axis is used to easily categorize the proposals in the existing literature. In this way, this work also represents a simple summary of the current state-of-the-art to assess the interpretability of Linguistic Fuzzy Rule-Based Systems.},
	isbn = {978-3-642-23713-3},
	author = {M. J. Gacto and Alcal{\'a}, R. and F. Herrera},
	editor = {Fanelli, Anna Maria and Pedrycz, Witold and Petrosino, Alfredo}
}
@article {simidat215,
	title = {A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {19},
	number = {4},
	year = {2011},
	pages = {666-681},
	doi = {10.1109/TFUZZ.2011.2131657 COMPLEMENTARY MATERIAL to the paper here: dataset partitions, results, figures, etc..},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@article {simidat235,
	title = {A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position},
	journal = {International Journal of Approximate Reasoning},
	volume = {52},
	number = {6},
	year = {2011},
	pages = {751-766},
	doi = {10.1016/j.ijar.2011.0},
	author = {J. Sanz and A. Fern{\'a}ndez and H. Bustince and F. Herrera}
}
@article {Rivera2011,
	title = {A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods},
	journal = {Applied Intelligence},
	volume = {34},
	number = {3},
	year = {2011},
	month = {Jun},
	pages = {331{\textendash}346},
	abstract = {Time series forecasting is an important task for the business sector. Agents involved in the olive oil sector consider that, for the olive oil price, medium-term predictions are more important than short-term predictions. In collaboration with these agents the forecasting of the price of extra-virgin olive oil six months ahead has been established as the aim of this work. According to expert opinion, the use of exogenous variables and technical indicators can help in this task and must be included in the forecasting process. The amount of variables that can be considered makes necessary the use of feature selection algorithms in order to reduce the number of variables and to increase the interpretability and usefulness of the obtained forecasting system. Thus, in this paper CO2RBFN, a cooperative-competitive algorithm for Radial Basis Function Network design, and other soft computing methods have been applied to the data sets with the whole set of input variables and to the data sets with the selected set of input variables. The experimentation carried out shows that CO2RBFN obtains the best results in medium term forecasting for olive oil prices with the whole and with the selected set of input variables. Moreover, the feature selection methods applied to the data sets highlighted some influential variables which could be considered not only for the prediction but also for the description of the complex process involved in the medium-term forecasting of the olive oil price.},
	issn = {0924-669x},
	doi = {10.1007/s10489-011-0284-1},
	url = {https://doi.org/10.1007/s10489-011-0284-1},
	author = {A.J. Rivera-Rivas and P{\'e}rez-Recuerda, Pedro and M.D. P{\'e}rez-Godoy and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel}
}
@conference {10.1007/978-3-642-25274-7_27,
	title = {A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price},
	booktitle = {Advances in Artificial Intelligence},
	year = {2011},
	pages = {263{\textendash}272},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper we present a summary of the application of CO2RBFN, a evolutionary cooperative-competitive algorithm for Radial Basis Function Networks design, to the medium-term forecasting of the extra-virgen olive price, carry out by the SIMIDAT research group. The forecast is about the price at source of the extra-virgin olive oil six months ahead. The influential of the feature selection algorithms over the forecasting of the extra-virgin olive oil price has been analysed in this study and the results obtained with CO2RBFN have been compared with those obtained by different soft computing methods.},
	isbn = {978-3-642-25274-7},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus and P{\'e}rez-Recuerda, Pedro and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Lozano, Jose A. and G{\'a}mez, Jos{\'e} A. and Moreno, Jos{\'e} A.}
}
@article {simidat238,
	title = {Addressing Data Complexity for Imbalanced Data Sets: Analysis of SMOTE-based Oversampling and Evolutionary Undersampling},
	journal = {Soft Computing},
	volume = {15},
	number = {10},
	year = {2011},
	pages = {1909-1936},
	doi = {10.1007/s00500-010-0625-8},
	author = {J. Luengo and A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera}
}
@article {Hcgd11,
	title = {An overview on Subgroup Discovery: Foundations and Applications},
	journal = {Knowledge and Information Systems},
	volume = {29},
	number = {3},
	year = {2011},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {495-525},
	doi = {10.1007/s10115-010-0356-2},
	author = {F. Herrera and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {simidat221,
	title = {Analysis of the Impact of Using Different Diversity Functions for the Subgroup Discovery Algorithm NMEEF-SD},
	booktitle = {5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2011},
	month = {April},
	pages = {17-23},
	address = {Paris (France)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@conference {simidat220,
	title = {Descubrimiento de subgrupos mediante sistemas difusos evolutivos},
	booktitle = {II Jornadas Andaluzas de Inform{\'a}tica (JAI)},
	year = {2011},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	month = {September},
	pages = {30-35},
	address = {Canillas del Aceituno (Spain)},
	author = {C. J. Carmona}
}
@article {Cgdnj11,
	title = {Evolutionary Fuzzy Rule Extraction for Subgroup Discovery in a Psychiatric Emergency Department},
	journal = {Soft Computing},
	volume = {15},
	number = {12},
	year = {2011},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	pages = {2435-2448},
	doi = {10.1007/s00500-010-0670-3},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and M. Nav{\'\i}o and L. Jim{\'e}nez}
}
@conference {simidat205,
	title = {Evolutionary Multi-Objective Algorithm to Effectively Improve the Performance of the Classic Tuning of Fuzzy Logic Controllers for a Heating, Ventilating and Air Conditioning System},
	booktitle = {5th International Workshop On Genetic And Evolutionary Fuzzy Systems (GEFS)},
	year = {2011},
	month = {April},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {Dglch11,
	title = {Evolutionary Selection of Hyperrectangles in Nested Generalized Exemplar Learning},
	journal = {Applied Soft Computing},
	volume = {11},
	number = {3},
	year = {2011},
	note = {TIN2008-06681-C06-01},
	pages = {3032-3045},
	doi = {10.1016/j.asoc.2010.11.030},
	author = {S. Garc{\'\i}a and J. Derrac and J. Luengo and C. J. Carmona and F. Herrera}
}
@article {article,
	title = {Future Performance Modeling in Athletism with Low Quality Data-based Genetic Fuzzy Systems},
	journal = {Multiple-Valued Logic and Soft Computing},
	volume = {17},
	year = {2011},
	month = {01},
	pages = {207-228},
	author = {Palacios, Ana and S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@article {COUSO201168,
	title = {Inner and outer fuzzy approximations of confidence intervals},
	journal = {Fuzzy Sets and Systems},
	volume = {184},
	number = {1},
	year = {2011},
	note = {Preference Modelling and Decision Analysis (Selected Papers from EUROFUSE 2009)},
	pages = {68 - 83},
	abstract = {We extend the notion of confidence region to fuzzy data, by defining a pair of fuzzy inner and outer confidence regions. We show the connection with previous proposals, as well as with recent studies on hypothesis testing with low quality data.},
	keywords = {Confidence region, Hypothesis testing, Possibility measure},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2010.11.004},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011410004550},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@article {simidat216,
	title = {Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures},
	journal = {Information Sciences},
	volume = {181},
	number = {20},
	year = {2011},
	pages = {4340{\textendash}4360},
	doi = {10.1016/j.ins.2011.02.021 COMPLEMENTARY MATERIAL to the paper here: links to the papers with doi, new contributions, etc..},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@inbook {677,
	title = {Introducci{\'o}n de informaci{\'o}n geogr{\'a}fica en terrenos 3D},
	booktitle = {Gesti{\'o}n de informaci{\'o}n urbana tridimensional},
	year = {2011},
	pages = {71-82},
	issn = {978-84-694-8374-9},
	author = {Aguilera-Garc{\'\i}a, {\'A}ngel and Jim{\'e}nez-Perez, Juan Roberto and Francisco Mart{\'\i}nez}
}
@article {simidat237,
	title = {KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework},
	journal = {Journal of Multiple-Valued Logic and Soft Computing},
	volume = {17},
	number = {2-3},
	year = {2011},
	pages = {255-287},
	author = {J. Alcal{\'a}-Fdez and A. Fern{\'a}ndez and J. Luengo and J. Derrac and S. Garc{\'\i}a and L. S{\'a}nchez and F. Herrera}
}
@article {PALACIOS2011841,
	title = {Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data},
	journal = {International Journal of Approximate Reasoning},
	volume = {52},
	number = {6},
	year = {2011},
	pages = {841 - 862},
	abstract = {Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed on low quality data, in an interval or a fuzzy number. A cooperative-competitive genetic algorithm searches for the knowledge base whose fitness is primal with respect to a precedence relation between the values of this interval or fuzzy valued risk. In addition to this, the numerical estimation of this risk depends on the entrywise product of cost and confusion matrices. These have been, in turn, generalized to vague data. The flexible assignment of values to the cost function is also tackled, owing to the fact that the use of linguistic terms in the definition of the misclassification cost is allowed.},
	keywords = {Cost sensitive classification, genetic fuzzy system, Low Quality Data},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2011.02.007},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X11000545},
	author = {Ana M. Palacios and Luciano S{\'a}nchez and In{\'e}s Couso}
}
@conference {inproceedings,
	title = {Managing stochastic algorithms cross-validation variability using an interval valued multiple comparison procedure},
	booktitle = {International Conference on Intelligent Systems Design and Applications},
	year = {2011},
	month = {11},
	pages = {1391-1396},
	doi = {10.1109/ISDA.2011.6121854},
	author = {Otero, Jos{\'e} and S{\'a}nchez, Luciano and Palacios, Ana and Couso, In{\'e}s}
}
@article {COUSO2011240,
	title = {Mark-recapture techniques in statistical tests for imprecise data},
	journal = {International Journal of Approximate Reasoning},
	volume = {52},
	number = {2},
	year = {2011},
	note = {Philosophy of Probability},
	pages = {240 - 260},
	abstract = {We aim to construct suitable tests when we have imprecise information about a sample. More specifically, we assume that we get a collection of n sets of values, each one characterizing an imprecise measurement. Each set specifies where the true sample value is (and where it is not) with full confidence, but it does not provide any additional information. Our main objectives are twofold: first we will review different kinds of tests in the literature about inferential statistics with random sets and discuss the approach that best suits our definition of imprecise data. Secondly, we will show that we can take advantage from mark and recapture techniques to improve the accuracy of our decisions. These techniques will be specially important when the population is small enough (with respect to the sample size) that recaptures are common. They also seem to be useful when resampling techniques are involved in the decision process.},
	keywords = {Hypothesis testing, Imprecise data analysis, Multi-valued mappings, Multi-valued test function, Probability bounds},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2010.07.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X10000952},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@conference {325,
	title = {Multi-label Testing for CO2RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification},
	booktitle = {11th International Work-Conference on Artificial Neural Networks, IWANN 2011},
	year = {2011},
	note = {TIN2008-06681-C06-02,TIC-3928},
	month = {6},
	pages = {41{\textendash}48},
	address = {Torremolinos-M{\'a}laga (Spain)},
	abstract = {While in traditional classification an instance of the data set is only associated with one class, in multi-label classification this instance can be associated with more than one class or label. Examples of applications in this growing area are text categorization, functional genomics and association of semantic information to audio or video content. One way to address these applications is the Problem Transformation methodology that transforms the multi-label problem into one single-label classification problem, in order to apply traditional classification methods. The aim of this contribution is to test the performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs, in a multi-label environment, using the problem transformation methodology. The results obtained by CO2RBFN, and by other classical data mining methods, show that no algorithm outperforms the other on all the data.},
	isbn = {978-3-642-21501-8},
	doi = {10.1007/978-3-642-21501-8_6},
	author = {A.J. Rivera-Rivas and Francisco Charte and M.D. P{\'e}rez-Godoy and M. J. del Jesus}
}
@article {article,
	title = {Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels},
	journal = {Soft Comput.},
	volume = {15},
	year = {2011},
	month = {10},
	pages = {1945-1957},
	doi = {10.1007/s00500-010-0627-6},
	author = {S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@article {article,
	title = {On the discovery of association rules by means of evolutionary algorithms},
	journal = {Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery},
	volume = {1},
	number = {5},
	year = {2011},
	month = {09},
	pages = {397-415},
	issn = {1942-4795},
	doi = {10.1002/widm.18},
	author = {M. J. del Jesus and G{\'a}mez, Jos{\'e} and P. Gonz{\'a}lez and Puerta, Jose}
}
@inbook {678,
	title = {Operaciones Booleanas sobre pol{\'\i}gonos},
	booktitle = {Gesti{\'o}n de informaci{\'o}n urbana tridimiensional},
	year = {2011},
	pages = {101-110},
	publisher = {Edici{\'o}n propia},
	organization = {Edici{\'o}n propia},
	address = {Ja{\'e}n (Espa{\~n}a)},
	issn = {978-84-694-8374-9},
	author = {Francisco Mart{\'\i}nez and Aguilera-Garc{\'\i}a, {\'A}ngel and Jim{\'e}nez-Perez, Juan Roberto}
}
@article {article,
	title = {Singular spectral analysis of ill-known signals and its application to predictive maintenance of windmills with SCADA records},
	journal = {Soft Computing},
	volume = {16},
	year = {2011},
	month = {05},
	doi = {10.1007/s00500-011-0767-3},
	author = {S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@conference {simidat222,
	title = {Subgroup discovery in an e-learning usage study based on Moodle},
	booktitle = {International Conference on EUropean Transnational Education (ICEUTE)},
	year = {2011},
	note = {TIN-2008-06681-C06-02,TIN-2008-06681-C06-03,TIC-3928,TIC-3720},
	month = {October},
	pages = {26-31},
	address = {Salamanca (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and S. Ventura}
}
@article {625,
	title = {Subgroup Discovery: Real-World Applications},
	year = {2011},
	pages = {1-21},
	institution = {De Montfort University},
	address = {Leicester, United Kingdom},
	author = {C. J. Carmona and D. Elizondo}
}
@conference {inproceedings,
	title = {The Magina Project. The Renewables Potential for Electricity Production in the Province of Jaen, Southern Spain},
	year = {2011},
	month = {11},
	pages = {3452-3459},
	isbn = {978-91-7393-070-3},
	doi = {10.3384/ecp110573452},
	author = {Terrados, Julio and Ruiz-Arias, Jose and Hontoria, L and Almonacid, G and Perez-Higueras, Pedro and Pozo-Vazquez, D and Gallego, F.J. and Gomez, P and Castro, Eulogio and Martin-Mesa, A and M. J. del Jesus}
}
@conference {6007647,
	title = {Using the Adaboost algorithm for extracting fuzzy rules from low quality data: Some preliminary results},
	booktitle = {2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)},
	year = {2011},
	month = {June},
	pages = {1263-1270},
	abstract = {When the Adaboost algorithm is used for extracting fuzzy rules from data, each rule is regarded as a weak learner, and knowledge bases as assimilated to ensembles. In this paper we propose an extension of this framework for obtaining fuzzy rule-based classifiers from imprecise data. In the new approach, the mentioned search of the best rule at each iteration is carried out by a genetic algorithm with a fuzzy fitness function. The instances will be assigned fuzzy weights, however each fuzzy rule will be associated to a crisp number of votes.},
	keywords = {Adaboost algorithm, Boosting, data handling, Electronic mail, fuzzy fitness function, fuzzy rule extraction, fuzzy rule-based classifiers, fuzzy set theory, Fuzzy systems, fuzzy weights, genetic algorithm, genetic algorithms, Genetic Fuzzy Systems, knowledge based systems, knowledge bases, learning (artificial intelligence), Low Quality Data, Merging, Optimization, pattern classification, Training},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2011.6007647},
	author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@inbook {676,
	title = {Visualizaci{\'o}n de medios participativos en entornos urbanos},
	booktitle = {Gesti{\'o}n de informaci{\'o}n urbana tridimiensional},
	year = {2011},
	pages = {151-157},
	edition = {Edici{\'o}n propia},
	issn = {978-84-694-8374-9},
	author = {Jim{\'e}nez-Perez, Juan Roberto and Francisco Mart{\'\i}nez and Aguilera-Garc{\'\i}a, {\'A}ngel}
}
@conference {article,
	title = {A coevolutionary genetic algorithm able to learn a FRBS and detect the less informative instances in low quality problems},
	booktitle = {Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy},
	year = {2010},
	month = {01},
	address = {Huelva (Espa{\~n}a)},
	author = {S{\'a}nchez, Luciano}
}
@conference {586,
	title = {A Preliminary Study on Mutation Operators in Cooperative Competitive Algorithms for RBFN Design},
	booktitle = {IEEE World Congress on Computational Intelligence (WCCI)},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928},
	pages = {349{\textendash}355},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and C. J. Carmona and M. J. del Jesus}
}
@conference {simidat206,
	title = {A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification},
	booktitle = {Twenty Third International Conference on Industrial, Engineering \& Other Applications of Applied Intelligent Systems (IEA/AIE)},
	year = {2010},
	note = {TIN2008-06681-C06-01,TIN2008-06681-C06-02},
	pages = {601-610},
	address = {Cordoba},
	author = {S. Garc{\'\i}a and J. Derrac and I. Triguero and C. J. Carmona and F. Herrera}
}
@inbook {GarciaCH10,
	title = {A Review on Evolutionary Prototype Selection},
	booktitle = {Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications},
	year = {2010},
	pages = {92{\textendash}113},
	doi = {10.4018/978-1-60566-798-0.ch005},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@article {simidat187,
	title = {Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power},
	journal = {Information Sciences},
	volume = {180},
	year = {2010},
	pages = {2044{\textendash}2064},
	doi = {10.1016/j.ins.2009.12.010},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and J. Luengo and F. Herrera}
}
@conference {simidat190,
	title = {Analysing the Hierarchical Fuzzy Rule Based Classification Systems with Genetic Rule Selection},
	booktitle = {4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2010},
	month = {March},
	pages = {69-74},
	address = {Mieres (Spain)},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets},
	journal = {Pattern Recognition Letters},
	volume = {31},
	number = {15},
	year = {2010},
	month = {11},
	pages = {2375-2388},
	issn = {0167-8655},
	doi = {10.1016/j.patrec.2010.07.010},
	author = {M.D. P{\'e}rez-Godoy and Fern{\'a}ndez, Alberto and Rivera Rivas, Antonio and M. J. del Jesus}
}
@conference {simidat192,
	title = {Analysis of the Performance of a Semantic Interpretability-Based Tuning and Rule Selection of Fuzzy Rule-Based Systems by Means of a Multi-Objective Evolutionary Algorithm},
	booktitle = {LNAI 6097},
	year = {2010},
	month = {June},
	pages = {228-238},
	address = {C{\'o}rdoba},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {article,
	title = {Applying multiobjective RBFNNs optimization and feature selection to a mineral reduction problem},
	journal = {Expert Syst. Appl.},
	volume = {37},
	year = {2010},
	month = {06},
	pages = {4050-4057},
	doi = {10.1016/j.eswa.2009.11.056},
	author = {Guill{\'e}n, Alberto and Rubio, Gines and Toda, I and Rivera Rivas, Antonio and Pomares, Hector and Rojas Ruiz, Ignacio}
}
@conference {Snchez2010AssessingTE,
	title = {Assessing the evolution of learning capabilities and disorders with graphical exploratory analysis of surveys containing missing and conflicting answers},
	year = {2010},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and Jos{\'e} Varela Otero and Ana M. Palacios}
}
@inbook {587,
	title = {CO2RBFN for Short and Medium Term Forecasting of the Extra Virgin Olive Oil Price},
	booktitle = {Studies in Computational Intelligence},
	volume = {284},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928, UJA-08-16-30.},
	pages = {113-125},
	issn = {978-3-642-12538-6},
	author = {M.D. P{\'e}rez-Godoy and P. P{\'e}rez and M.P. Fr{\'\i}as and A.J. Rivera-Rivas and C. J. Carmona and M. Parras}
}
@article {582,
	title = {CO2RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market},
	journal = {International Journal of Hybrid Intelligent Systems},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928, UJA-08-16-30},
	pages = {75-87},
	doi = {10.3233/HIS-2010-0106},
	author = {M.D. P{\'e}rez-Godoy and P. P{\'e}rez and A.J. Rivera-Rivas and M. J. del Jesus and C. J. Carmona and M.P. Fr{\'\i}as and M. Parras}
}
@article {755,
	title = {Comparison of response surface methodology and artificial neural network applied to enzymatic hydrolysis of rapeseed straw},
	journal = {Journal of Biotechnology},
	volume = {150},
	year = {2010},
	pages = {137},
	issn = {0168-1656},
	doi = {10.1016/j.patrec.2010.07.010},
	author = {E. Castro and C. Cara and M. J. del Jesus and V. M. Rivas}
}
@conference {simidat177,
	title = {Evolutionary algorithms for subgroup discovery applied to e-learning data},
	booktitle = {IEEE EDUCON},
	year = {2010},
	note = {TIN-2008-06681-C06-02,TIN-2008-06681-C06-03,TIC-3928,TIC-3720},
	month = {April},
	pages = {983-990},
	address = {Madrid (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and C. Romero and S. Ventura}
}
@article {simidat239,
	title = {Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {14},
	number = {6},
	year = {2010},
	pages = {913-941},
	doi = {10.1109/TEVC.2009.2039140},
	author = {A. Fern{\'a}ndez and J. Luengo and S. Garc{\'\i}a and E. Bernad{\'o}-Mansilla and F. Herrera}
}
@article {BERLANGA20101183,
	title = {GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems},
	journal = {Information Sciences},
	volume = {180},
	number = {8},
	year = {2010},
	pages = {1183 - 1200},
	abstract = {In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.},
	keywords = {classification, Fuzzy rule-based systems, Genetic Fuzzy Systems, Genetic programming, High-dimensional problems, Interpretability-accuracy trade-off},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2009.12.020},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025509005635},
	author = {F.J. Berlanga and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {simidat185,
	title = {Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning},
	journal = {Information Sciences},
	volume = {180},
	number = {19},
	year = {2010},
	pages = {3674-3685},
	doi = {10.1016/j.ins.2010.06.018},
	author = {J. Sanz and A. Fern{\'a}ndez and H. Bustince and F. Herrera}
}
@conference {simidat174,
	title = {Indice de Interpretabilidad Sem{\'a}ntica para el Ajuste de Sistemas Basados en Reglas Difusas y Selecci{\'o}n de Reglas Mediante un Algoritmo Evolutivo Multi-Objetivo},
	booktitle = {XV Edici{\'o}n del Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2010},
	month = {February},
	pages = {73-78},
	address = {Huelva},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat173,
	title = {Integration of an Index to Preserve the Semantic Interpretability in the Multi-Objective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {18},
	number = {3},
	year = {2010},
	pages = {515-531},
	doi = {10.1109/TFUZZ.2010.2041008},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {10.1007/978-3-642-13022-9_21,
	title = {Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market},
	booktitle = {Trends in Applied Intelligent Systems},
	year = {2010},
	pages = {205{\textendash}214},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlighted. In this paper an adaptation of CO2RBFN, evolutionary COoperative-COmpetitive algorithm for Radial Basis Function Networks design, applied to the long-term prediction of the extra-virgin olive oil price is presented. This long-term horizon has been fixed to six months. The results of CO2RBFN have been compared with other data mining methods, typically used in time series forecasting, such as other neural networks models, a support vector machine method and a fuzzy system.},
	isbn = {978-3-642-13022-9},
	author = {M.D. P{\'e}rez-Godoy and P{\'e}rez, Pedro and A.J. Rivera-Rivas and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Garc{\'\i}a-Pedrajas, Nicol{\'a}s and F. Herrera and Fyfe, Colin and Ben{\'\i}tez, Jos{\'e} Manuel and Ali, Moonis}
}
@conference {5454152,
	title = {Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems},
	booktitle = {2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2010},
	month = {March},
	pages = {75-80},
	abstract = {Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow fuzzy classifiers to deal with high dimensional problems avoiding the curse of dimensionality. The present contribution establishes the first basis in this direction by introducing a genetic fuzzy system-based framework to build the fuzzy linguistic combination method for a bagging fuzzy multiclassification system.},
	keywords = {Bagging, bagging fuzzy multiclassification system, Classification tree analysis, classifier ensemble, Computer science, Decision trees, fuzzy aggregation operators, fuzzy linguistic combination methods, fuzzy linguistic rule-based system, Fuzzy reasoning, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy-based system, Genetics, knowledge based systems, learning (artificial intelligence), machine learning, Neural networks, pattern classification},
	doi = {10.1109/GEFS.2010.5454152},
	author = {L. S{\'a}nchez and O. Cord{\'o}n and A. Quirin and K. Trawinski}
}
@inbook {inbook,
	title = {Introduction to the Experimental Design in the Data Mining Tool KEEL},
	year = {2010},
	month = {01},
	pages = {1-25},
	isbn = {978-1-61520-757-2},
	author = {Alcala-Fdez, Jesus and Garcia, S and S{\'a}nchez, Luciano and Robles, I and M. J. del Jesus and Bernado-Mansilla, E and Peregrin, Antonio and F. Herrera}
}
@conference {583,
	title = {Mejoras en el Dise{\~n}o Multiobjetivo de Redes de Funciones de Base Radial},
	booktitle = {Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928.},
	pages = {441-446},
	author = {P.L. L{\'o}pez and A.J. Rivera-Rivas and C. J. Carmona and M.D. P{\'e}rez-Godoy}
}
@conference {simidat191,
	title = {Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning},
	booktitle = {13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	series = {Lecture Notes on Artificial Intelligence},
	volume = {6178},
	year = {2010},
	month = {June},
	pages = {89-98},
	address = {Dortmund (Germany)},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {Cgdh10,
	title = {NMEEF-SD: Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {18},
	number = {5},
	year = {2010},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {958-970},
	doi = {10.1109/TFUZZ.2010.2060200},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {simidat186,
	title = {On the 2-Tuples Based Genetic Tuning Performance for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets},
	journal = {Information Sciences},
	volume = {180},
	number = {8},
	year = {2010},
	pages = {1268-1291},
	doi = {10.1016/j.ins.2009.12.014},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@conference {article,
	title = {Potenciando el aprendizaje proactivo con ILIAS\&WebQuest: aprendiendo a paralelizar algoritmos con GPUs},
	booktitle = {Jornadas de ense{\~n}anza universitaria de la inform{\'a}tica},
	year = {2010},
	month = {01},
	address = {Santiago de Compostela (Espa{\~n}a)},
	author = {Santamar{\'\i}a, J and Espinilla, Macarena and Rivera Rivas, Antonio and Romero, Samuel}
}
@conference {simidat200,
	title = {Predicci{\'o}n de Series Temporales a largo plazo mediante Algoritmos Meta-evolutivos y Redes Neuronales de Funciones Base Radial},
	booktitle = {III Simposio de Inteligencia Computacional (SICO)},
	year = {2010},
	month = {September},
	pages = {49-56},
	address = {Valencia},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. Olmo and A. Fernandez-Montoya and M. Cillero}
}
@conference {5584797,
	title = {Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers},
	booktitle = {International Conference on Fuzzy Systems},
	year = {2010},
	month = {July},
	pages = {1-8},
	abstract = {When there is a substantial difference between the number of cases of the majority and minority classes, minimum error-based classification systems tend to overlook these last instances. This can be corrected either by preprocessing the dataset or by altering the objective function of the classifier. In this paper we analyze the first approach, in the context of genetic fuzzy systems (GFS), and in particular of those that can operate with imprecisely observed and low quality data. We will analyze the different preprocessing mechanisms of imbalanced datasets and will show the necessity of extending these for solving those problems where the data is both imprecise and im-balanced. In addition, we include a comprehensive description of a new algorithm, able to preprocess imprecise imbalanced datasets. Several real-world datasets are used to evaluate the proposal.},
	keywords = {Classification algorithms, Context, data handling, Euclidean distance, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy classifier, genetic fuzzy system, Genetics, imbalanced dataset preprocessing, minimum error based classification system, Nearest neighbor searches, objective function, pattern classification, Pediatrics, Training},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2010.5584797},
	author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@conference {simidat178,
	title = {Preselection of Neurostimulation waveforms for visual prostheses using genetic algorithms},
	booktitle = {3rd International Conference on Biomedical Electronics and Devices (BIODEVICES)},
	year = {2010},
	note = {TIN2007-60587,TIN2008-06893-C03-02,P07-TIC-02768,P06-TIC-02007,TIC-3928,UJA-08-16-10},
	month = {January},
	pages = {191-194},
	address = {Valencia},
	author = {S. Romero and A. Guill{\'e}n and C. J. Carmona and C. Morillas and F. Pelayo and H. Pomares}
}
@conference {326,
	title = {ReturnOK: El pasado de la computaci{\'o}n personal},
	booktitle = {V Congreso Internacional de Patrimonio e Historia de la Ingenier{\'\i}a},
	year = {2010},
	note = {-},
	month = {4},
	pages = {1{\textendash}19},
	address = {Las Palmas de Gran Canaria (Spain)},
	abstract = {La historia de la inform{\'a}tica o computaci{\'o}n es una parte de la historia humana y, al tiempo, una fuente de informaci{\'o}n indispensable sobre la evoluci{\'o}n del hardware y el software en las {\'u}ltimas d{\'e}cadas. Tambi{\'e}n resulta indispensable para entender el estado actual de las tecnolog{\'\i}as de la informaci{\'o}n en m{\'u}ltiples aspectos: el desarrollo de microprocesadores y la forma en que fueron surgiendo los sistemas digitales, el nacimiento de la microinform{\'a}tica personal (que represent{\'o} el acceso cuasi-universal a los ordenadores), el dise{\~n}o de software a trav{\'e}s de los lenguajes que fueron surgiendo, c{\'o}mo estos han ido adapt{\'a}ndose en cada momento y los actores m{\'a}s relevantes que han influido de manera decisiva en este campo. Con el fin de difundir la historia de los precursores de los PCs (los microcomputadores de los a{\~n}os 70 y 80) se ha creado una wiki tem{\'a}tica a la que se ha denominado ReturnOK: el museo digital del PC (http://museopc.ujaen.es). Este sitio web es el resultado de un proyecto de innovaci{\'o}n docente que se empez{\'o} a dise{\~n}ar y desarrollar en 2007. Desde esta wiki (un espacio colaborativo) cualquier aficionado a la retroinform{\'a}tica puede conocer la evoluci{\'o}n de la inform{\'a}tica y contribuir con sus conocimientos y recursos multimedia desde cualquier navegador Web. Esta wiki ofrece informaci{\'o}n relativa al hardware hist{\'o}rico (descripciones, fichas t{\'e}cnicas, fotograf{\'\i}as, etc.), el software utilizado en dicho hardware, emuladores que hacen posible ejecutarlos en dispositivos actuales (ordenadores y tel{\'e}fonos m{\'o}viles), biograf{\'\i}as, publicaciones de la {\'e}poca y bibliograf{\'\i}a relacionada. Se convierte, por tanto, en un medio de transferencia de conocimiento y de aprendizaje adicional a los ya cl{\'a}sicos, {\'u}til tanto para los estudios espec{\'\i}ficos de inform{\'a}tica como para otros afines: electr{\'o}nica, industrial, etc.},
	isbn = {84-920788-4},
	author = {Lina Garc{\'\i}a and Ildefonso Ruano and Francisco Charte and Andr{\'e}s Molina and Jos{\'e} R. Balsas}
}
@article {327,
	title = {ReturnOK, la Wiki sobre retroinform{\'a}tica},
	journal = {Iniciaci{\'o}n a la Investigaci{\'o}n},
	volume = {5},
	year = {2010},
	pages = {1-5},
	abstract = {En este art{\'\i}culo se describe el p{\'o}ster que resume las aportaciones del Proyecto de Innovaci{\'o}n Docente de la Universidad de Ja{\'e}n (convocatoria 2007-2009) que se denomin{\'o} "Museo digital sobre precursores del PC. Desarrollo, implementaci{\'o}n y explotaci{\'o}n de una wiki tem{\'a}tica". Fue financiado con 3000 euros por el Secretariado de Innovaci{\'o}n Docente dependiente del Vicerrectorado de Ordenaci{\'o}n Acad{\'e}mica, Innovaci{\'o}n Docente y Profesorado. Este proyecto ha sido galardonado en la I Convocatoria de premios a la Innovaci{\'o}n Docente de la Universidad de Ja{\'e}n 2010 con el Premio en el {\'A}rea de Tecnolog{\'\i}a.},
	issn = {1988-415X},
	author = {Lina Garc{\'\i}a Cabrera and Ildefonso Ruano and Francisco Charte and Andr{\'e}s Molina Aguilar and Jos{\'e} Ram{\'o}n Balsas Almagro}
}
@conference {Martinez2010,
	title = {Simulating multi-self-similar spatiotemporal models with CUDA},
	booktitle = {COMPSTAT 2010},
	year = {2010},
	pages = {341-341},
	address = {Par{\'\i}s, del 22 al 27 de Agosto},
	author = {Francisco Mart{\'\i}nez and Mar{\'\i}a Pilar Fr{\'\i}as-Bustamante and Mar{\'\i}a Dolores Ruiz-Medina}
}
@article {simidat184,
	title = {Solving Multi-Class Problems with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning and Preference Relations},
	journal = {Fuzzy Sets and Systems},
	volume = {161},
	number = {23},
	year = {2010},
	pages = {3064-3080},
	doi = {10.1016/j.fss.2010.05.016},
	author = {A. Fern{\'a}ndez and M. Calder{\'o}n and E. Barrenechea and H. Bustince and F. Herrera}
}
@article {article,
	title = {The multi-LREP decomposition of solids and its application to a point-in-polyhedron inclusion test},
	journal = {The Visual Computer},
	volume = {26},
	year = {2010},
	month = {11},
	pages = {1361-1368},
	doi = {10.1007/s00371-009-0413-6},
	author = {Francisco Mart{\'\i}nez and Rueda, Antonio and Feito, Francisco}
}
@conference {simidat201,
	title = {Time Series forecasting: Automatic Determination of Lags and Radial Basis Neural Networks for a Changing Horizon Environment},
	booktitle = {2010 IEEE World Congress on Computational Intelligence},
	year = {2010},
	month = {July},
	pages = {4023-4029},
	address = {Barcelona (Spain)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas}
}
@conference {inproceedings,
	title = {A Baseline Learning Genetic Fuzzy Classifier Based on Low Quality Data.},
	year = {2009},
	month = {01},
	pages = {803-808},
	author = {Palacios, Ana and S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@conference {10.1007/978-3-642-02319-4_79,
	title = {A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data},
	booktitle = {Hybrid Artificial Intelligence Systems},
	year = {2009},
	pages = {654{\textendash}661},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Minimum risk classification problems use a matrix of weights for defining the cost of misclassifying an object. In this paper we extend a simple genetic fuzzy system (GFS) to this case. In addition, our method is able to learn minimum risk fuzzy rules from low quality data. We include a comprehensive description of the new algorithm and discuss some issues about its fuzzy-valued fitness function. A synthetic problem, plus two real-world datasets, are used to evaluate our proposal.},
	isbn = {978-3-642-02319-4},
	author = {Palacios, Ana M. and S{\'a}nchez, Luciano and Couso, In{\'e}s},
	editor = {Corchado, Emilio and Wu, Xindong and Oja, Erkki and Herrero, {\'A}lvaro and Baruque, Bruno}
}
@conference {simidat151,
	title = {A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability},
	booktitle = {Proceedings of the Joint International Fuzzy Systems Association World Congress and the European Society for Fuzzy Logic and Technology Conference (IFSA)},
	year = {2009},
	month = {July},
	pages = {1146-1151},
	publisher = {IFSA/EUSFLAT 2009},
	organization = {IFSA/EUSFLAT 2009},
	address = {Lisbon, Portugal},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {MARTINEZ20091177,
	title = {A new algorithm for computing Boolean operations on polygons},
	journal = {Computers \& Geosciences},
	volume = {35},
	number = {6},
	year = {2009},
	pages = {1177 - 1185},
	abstract = {This paper presents a new algorithm for computing Boolean operations on polygons. These kind of operations are frequently used in the geosciences in order to get spatial information from spatial data modeled as polygons. The presented algorithm is simple and easy to understand and implement. Let n be the total number of edges of all the polygons involved in a Boolean operation and k be the number of intersections of all the polygon edges. Our algorithm computes the Boolean operation in time O((n+k)log(n)). Finally, the proposed algorithm works with concave polygons with holes, and with regions composed of polygon sets. Furthermore, it can be easily adapted to work with self-intersecting polygons.},
	keywords = {Boolean operations polygons, Polygon clipping, Polygon overlay},
	issn = {0098-3004},
	doi = {https://doi.org/10.1016/j.cageo.2008.08.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0098300408002793},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda and Francisco Ram{\'o}n Feito}
}
@conference {10.1007/978-3-642-02478-8_8,
	title = {A Preliminar Analysis of CO2RBFN in Imbalanced Problems},
	booktitle = {Bio-Inspired Systems: Computational and Ambient Intelligence},
	year = {2009},
	pages = {57{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs applied to classification problems on imbalanced domains and to study the cooperation of a well known preprocessing method, the {\textquoteleft}{\textquoteleft}Synthetic Minority Over-sampling Technique{\textquoteright}{\textquoteright} (SMOTE) with our algorithm. The good performance of CO2RBFN is shown through an experimental study carried out over a large collection of imbalanced data-sets.},
	isbn = {978-3-642-02478-8},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fern{\'a}ndez, A. and M. J. del Jesus and F. Herrera},
	editor = {Cabestany, Joan and Sandoval, Francisco and Prieto, Alberto and Corchado, Juan M.}
}
@conference {10.1007/978-3-540-85863-8_79,
	title = {A Symbiotic CHC Co-evolutionary Algorithm for Automatic RBF Neural Networks Design},
	booktitle = {International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)},
	year = {2009},
	pages = {663{\textendash}671},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {This paper introduces Symbiotic{\_}CHC{\_}RBF, a co-evolutionary algorithm intended to automatically establish the parameters needed to design models for classification problems. Co-evolution involves two populations, which evolve together by means of a symbiotic relationship. One of the populations is the method EvRBF, which provides the design of radial basis function neural nets by means of evolutionary algorithms. The second population evolves sets of parameters for the method EvRBF, being every individual of the population a configuration of parameters for the method. Results show that Symbiotic{\_}CHC{\_}RBF can be effectively used to obtain good models, while reducing significantly the number of parameters to be fixed by hand.},
	isbn = {978-3-540-85863-8},
	author = {E. Parras-Guti{\'e}rrez and M. J. del Jesus and Merelo, Juan J. and Rivas, Victor M.},
	editor = {Corchado, Juan M. and Rodr{\'\i}guez, Sara and Llinas, James and Molina, Jos{\'e} M.}
}
@conference {584,
	title = {Adaptaci{\'o}n de una asignatura avanzada de redes de computadores al modelo de docencia virtual dentro del marco del Espacio Europeo de Educaci{\'o}n Superior},
	booktitle = {International Conference on Development and Innovation with New Technologies in Engineering Education},
	year = {2009},
	pages = {15-21},
	author = {A.J. Rivera-Rivas and C. J. Carmona and M.D. P{\'e}rez-Godoy and M. J. del Jesus}
}
@article {simidat98,
	title = {Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems},
	journal = {Soft Computing},
	volume = {13},
	number = {5},
	year = {2009},
	pages = {419-436},
	doi = {10.1007/s00500-008-0359-z},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat199,
	title = {Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5},
	booktitle = {9th International Conference on Intelligent Systems Designs and Applications (ISDA)},
	year = {2009},
	pages = {523-528},
	author = {J. Luengo and A. Fern{\'a}ndez and F. Herrera and S. Garc{\'\i}a}
}
@conference {simidat113,
	title = {Algoritmo Gen{\'e}tico Multi-Objetivo Avanzado para el ajuste de un sistema difuso aplicado al Control de Sistemas de Ventilaci{\'o}n, Calefacci{\'o}n y Aire Acondicionado},
	booktitle = {Proceedings of the Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {595-602},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat67,
	title = {Algoritmos simbi{\'o}ticos para la generaci{\'o}n de redes RBF: comparativa entre codificaciones real y binaria},
	booktitle = {VI Congreso Espa{\~n}ol sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {341-348},
	address = {M{\'a}laga},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus}
}
@conference {simidat156,
	title = {An analysis of evolutionary algorithms with different types of fuzzy rules in subgroup discovery},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2009},
	note = {TIN2008-06681-C06-01,TIN2008-06681-C06-02,TIC-3928},
	month = {August},
	pages = {1706-1711},
	address = {ICC Jeju, Jeju Island, Korea},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {CO2RBFN: An evolutionary cooperative-competitive RBFN design algorithm for classification problems},
	journal = {Soft Computing},
	volume = {14},
	year = {2009},
	month = {07},
	pages = {953-971},
	doi = {10.1007/s00500-009-0488-z},
	author = {M.D. P{\'e}rez-Godoy and Rivera Rivas, Antonio and J. Berlanga, Francisco and M. J. del Jesus}
}
@conference {simidat153,
	title = {Designing Radial Basis Function Neural Networks with Meta-Evolutionary Algorithms: The Effect of Chromosome Codification},
	booktitle = {International Work-Conference on Artificial Neural Networks (IWANN)},
	year = {2009},
	month = {June},
	pages = {37-40},
	publisher = {LNCS 5518},
	organization = {LNCS 5518},
	address = {Salamanca (Spain)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus and Juan J. Merelo}
}
@article {simidat61,
	title = {Diagnose of Effective Evolutionary Prototype Selection using an Overlapping Measure},
	journal = {International Journal of Pattern Recognition and Artificial Intelligence},
	volume = {23},
	number = {8},
	year = {2009},
	pages = {1527-1548},
	author = {S. Garc{\'\i}a and J. R. Cano and E. Bernad{\'o}-Mansilla and F. Herrera}
}
@inbook {683,
	title = {Diplomatura de estad{\'\i}stica e ingenier{\'\i}a t{\'e}cnica de inform{\'a}tica de gesti{\'o}n},
	booktitle = {Planes de acci{\'o}n tutorial en la universidad},
	year = {2009},
	pages = {249-270},
	publisher = {Universidad de Ja{\'e}n, Servicio de publicaciones},
	organization = {Universidad de Ja{\'e}n, Servicio de publicaciones},
	address = {Ja{\'e}n},
	issn = {978-84-8439-436-5},
	author = {S{\'a}nchez-G{\'o}mez, Mar{\'\i}a del Carmen and Jos{\'e} Aguilera and Cano-Ortega, Antonio}
}
@conference {585,
	title = {EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design},
	booktitle = {International Work-Conference on Artificial Neural Networks (IWANN)},
	number = {2009},
	year = {2009},
	note = {TIN2008-06681-C06-02, TIN2007-60587},
	pages = {752{\textendash}759},
	publisher = {Lecture Notes in Computer Science 5517},
	organization = {Lecture Notes in Computer Science 5517},
	author = {P.L. L{\'o}pez and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus and C. J. Carmona}
}
@conference {simidat167,
	title = {Enhanced Radial Basis Function Neural Network Design Using Parallel Evolutionary Algorithms},
	booktitle = {11th International Conference on Engineering Applications of Neural Networks (EANN)},
	year = {2009},
	month = {August},
	pages = {269-280},
	address = {London},
	author = {E. Parras-Guti{\'e}rrez and M. Garc{\'\i}a-Arenas and V. M. Rivas}
}
@article {simidat145,
	title = {Enhancing the Effectiveness and Interpretability of Decision Tree and Rule Induction Classifiers with Evolutionary Training Set Selection over Imbalanced Problems},
	journal = {Applied Soft Computing},
	volume = {9},
	year = {2009},
	pages = {1304-1314},
	doi = {10.1016/j.asoc.2009.04.004},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and F. Herrera}
}
@conference {simidat168,
	title = {E-tsRBF: Automatic determination of lags to forecast time series by means of Radial Basis Function Neural Networks},
	booktitle = {I International Workshop on Mining of Non-Conventional Data (MINCODA)},
	year = {2009},
	month = {November},
	pages = {42-49},
	address = {Sevilla (Spain)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas}
}
@conference {simidat169,
	title = {E-tsRBF: preliminary results on the simultaneous determination of time-lags and parameters of Radial Basis Function Neural Networks for time series forecasting},
	booktitle = {9th International Conference on Intelligent Systems Design and Applications},
	year = {2009},
	month = {December},
	pages = {1445-1449},
	address = {Pisa (Italy)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus}
}
@article {simidat47,
	title = {Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data},
	journal = {Expert Systems with Applications},
	volume = {36},
	year = {2009},
	pages = {1632-1644},
	author = {C. Romero and P. Gonz{\'a}lez and S. Ventura and M. J. del Jesus and F. Herrera}
}
@article {756,
	title = {Evolutionary and metaheuristics based data mining},
	journal = {Soft Computing: a fusion of methodologies and applications},
	volume = {13},
	year = {2009},
	pages = {209-212},
	issn = {1432-7643},
	doi = {10.1007/s00500-008-0373-1},
	author = {M. J. del Jesus and J.A. G{\'a}mez and J.M. Puerta}
}
@article {article,
	title = {Extending a simple genetic cooperative-competitive learning fuzzy classifier to low quality datasets},
	journal = {Evolutionary Intelligence},
	volume = {2},
	year = {2009},
	month = {11},
	pages = {73-84},
	doi = {10.1007/s12065-009-0024-1},
	author = {Palacios, Ana and S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@conference {simidat176,
	title = {Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets},
	booktitle = {13 th International Fuzzy Systems Association World Congress and 6th European Society for Fuzzy Logic and Tecnology Conference (IFSA-EUSFLAT)},
	year = {2009},
	pages = {42-47},
	address = {Lisbon (Portugal)},
	author = {A. Fern{\'a}ndez and F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@conference {inproceedings,
	title = {GFS-Based Analysis of Vague Databases in High Performance Athletics},
	year = {2009},
	month = {09},
	pages = {602-609},
	doi = {10.1007/978-3-642-04394-9_73},
	author = {Palacios, Ana and Couso, In{\'e}s and S{\'a}nchez, Luciano}
}
@conference {simidat170,
	title = {Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm},
	booktitle = {9th International Conference on Intelligent Systems Design and Applications (ISDA)},
	year = {2009},
	month = {November},
	pages = {109-114},
	address = {Pisa (Italy)},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat158,
	title = {Hierarchical fuzzy rule based classfication systems with genetic rule selection for imbalanced data-sets},
	journal = {International Journal of Approximate Reasoning},
	volume = {50},
	year = {2009},
	pages = {561-577},
	doi = {10.1016/j.ijar.2008.11.004},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {simidat100,
	title = {Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems},
	journal = {Applied Intelligence},
	volume = {31},
	number = {1},
	year = {2009},
	pages = {15-30},
	doi = {10.1007/s10489-007-0107-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat189,
	title = {Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-sets Using an Evolutionary Adaptive Inference System},
	booktitle = {10th International Work-Conference on Artificial Neural Networks (IWANN)},
	series = {Lecture Notes on Computer Science},
	volume = {5517},
	year = {2009},
	month = {June},
	pages = {294-301,},
	address = {Salamanca (Spain)},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {simidat95,
	title = {KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems},
	journal = {Soft Computing},
	volume = {13},
	number = {3},
	year = {2009},
	pages = {307-318},
	doi = {10.1007/s00500-008-0323-y},
	author = {J. Alcal{\'a}-Fdez and L. S{\'a}nchez and S. Garc{\'\i}a and M. J. del Jesus and S. Ventura and J.M. Garrell and J. Otero and C. Romero and J. Bacardit and V. M. Rivas and J.C. Fern{\'a}ndez and F. Herrera}
}
@article {simidat99,
	title = {Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms},
	journal = {Fuzzy Sets and Systems},
	volume = {160},
	number = {7},
	year = {2009},
	pages = {905-921},
	doi = {10.1016/j.fss.2008.05.012},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@article {606,
	title = {Modelo predictivo colaborativo de apoyo al diagn{\'o}stico en servicio de urgencias psiqui{\'a}tricas},
	journal = {Revista Ib{\'e}rica de Sistemas y Tecnolog{\'\i}as de la informaci{\'o}n},
	volume = {4},
	number = {4},
	year = {2009},
	pages = {29-42},
	issn = {1646-9895},
	author = {J. R. Cano and P. Gonz{\'a}lez and Jos{\'e} Aguilera and A.G. L{\'o}pez-Herrera and F. Herrera and M. Nav{\'\i}o and Jim{\'e}nez-Arriero Miguel Angel}
}
@conference {simidat148,
	title = {Non-dominated Multi-objective Evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery},
	booktitle = {Proceedings of the Fourth International Conference on Hybrid Artificial Intelligence Systems (HAIS)},
	series = {LNAI},
	volume = {5572},
	year = {2009},
	note = {TIN2008-06681-C06-01,TIN2008-06681-C06-02,TIC-3928},
	month = {June},
	pages = {573-580},
	publisher = {Springer},
	organization = {Springer},
	address = {Salamanca (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms},
	journal = {Soft Comput.},
	volume = {13},
	year = {2009},
	month = {03},
	pages = {467-479},
	doi = {10.1007/s00500-008-0362-4},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e} and Couso, In{\'e}s}
}
@article {simidat157,
	title = {On the influence of an adaptive inference system in fuzzy rule-based classification sytems for imbalanced data-sets},
	journal = {Expert Systems with Applications},
	volume = {36},
	number = {6},
	year = {2009},
	pages = {9805-9812},
	doi = {10.1016/j.eswa.2009.02.048},
	author = {A. Fern{\'a}ndez and F. Herrera and M. J. del Jesus}
}
@conference {simidat152,
	title = {Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms},
	booktitle = {International Work-Conference on Artificial Neural Networks (IWANN)},
	year = {2009},
	month = {June},
	pages = {383-390},
	publisher = {LNCS 5517},
	organization = {LNCS 5517},
	address = {Salamanca (Spain)},
	author = {M.G. Arenas and E. Parras-Guti{\'e}rrez and V. M. Rivas and P.A. Castillo and M. J. del Jesus and Juan J. Merelo}
}
@article {simidat69,
	title = {Study of the robustness of a meta-algorithm for the estimation of parameters in Radial Basis Function Neural Networks design},
	journal = {Neural Network World},
	volume = {19},
	number = {1},
	year = {2009},
	pages = {81-94},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus and Juan J. Merelo}
}
@conference {simidat68,
	title = {Symbpar vs Symbiotic_CHC_RBF: Dise{\~n}ando redes de base radial de forma coevolutiva y paralela},
	booktitle = {VI Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {231-238},
	address = {M{\'a}laga},
	author = {M. Garc{\'\i}a-Arenas and E. Parras-Guti{\'e}rrez and V. M. Rivas}
}
@article {VILLAR2009250,
	title = {Taximeter verification using imprecise data from GPS},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {22},
	number = {2},
	year = {2009},
	pages = {250 - 260},
	abstract = {Until recently, local governments in Spain were using machines with rolling cylinders for testing and verification of taximeters. However, the tyres condition can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device should be designed. In our opinion, the verification of a taximeter will not be reliable unless measurements taken on an actual taxi run are used. Global positioning system (GPS) sensors are intuitively well suited for this process, because they provide the position and the speed with independence from those car devices that are under test. Nevertheless, since GPS measurements are inherently imprecise, GPS-based sensors are difficult to homologate. In this paper we will show how these legal problems can be solved. We propose a method for computing an upper bound of the length of the trajectory, taking into account the vagueness of the GPS data. The uncertainty in the GPS data will be modelled by fuzzy techniques. The upper bound will be computed using a multiobjective evolutionary algorithm. The accuracy of the measurements will be improved further by combining it with restrictions based on the dynamic behavior of the vehicles.},
	keywords = {fuzzy fitness function, Genetic Fuzzy Systems, GPS, Metrology, vague data},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2008.07.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197608001267},
	author = {Jos{\'e} Villar and Adolfo Otero and Jos{\'e} Otero and Luciano S{\'a}nchez}
}
@article {Villar2009,
	title = {Taximeter verification with GPS and soft computing techniques},
	journal = {Soft Computing},
	volume = {14},
	number = {4},
	year = {2009},
	month = {Mar},
	pages = {405},
	abstract = {Until recently, local governments in Spain were using machines with rolling cylinders for verifying taximeters. However, the condition of the tires can lead to errors in the process and the mechanical construction of the test equipment is not compatible with certain vehicles. Thus, a new measurement device needs to be designed. In our opinion, the verification of a taximeter will not be reliable unless measurements taken on an actual taxi run are used. GPS sensors are intuitively well suited for this process, because they provide the position and the speed with independence of those car devices that are under test. But there are legal problems that make difficult the use of GPS-based sensors: GPS coordinate measurements do not match exactly real coordinates and, generally speaking, we are not given absolute tolerances. We can not know whether the maximum error is always lower than, for example, 7~m. However, we might know that 50{\%} of the measurements lie on a circle with a radius of 7~m, centered on the real position. In this paper we describe a practical application where these legal problems have been solved with soft computing based technologies. In particular, we propose to characterize the uncertainty in the GPS with fuzzy techniques, so that we can reuse certain recent algorithms, formerly intended for being used in genetic fuzzy systems, to this new context. Specifically, we propose a new method for computing an upper bound of the length of the trajectory, taking into account the vagueness of the GPS data. This bound will be computed using a modified multiobjective evolutionary algorithm, which can optimize a fuzzy valued function. The accuracy of the measurements will be improved further by combining it with restrictions based on the dynamic behavior of the vehicles.},
	issn = {1433-7479},
	doi = {10.1007/s00500-009-0414-4},
	url = {https://doi.org/10.1007/s00500-009-0414-4},
	author = {Villar, Jos{\'e} and Otero, Adolfo and Otero, Jos{\'e} and S{\'a}nchez, Luciano}
}
@conference {Martinez2009,
	title = {Tests de inclusi{\'o}n punto en objeto basados en {\'a}rboles de intervalos},
	booktitle = {Congreso Espa{\~n}ol de Inform{\'a}tica Gr{\'a}fica},
	year = {2009},
	pages = {57-64},
	address = {San Sebasti{\'a}n},
	isbn = {978-3-905673-72-7},
	doi = {10.2312/LocalChapterEvents/CEIG/CEIG09/057-064},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda-Ruiz and Francisco Ram{\'o}n Feito-Higueruela}
}
@conference {simidat93,
	title = {Un Primer Estudio sobre la Utilizaci{\'o}n de Selecci{\'o}n Evolutiva de Conjuntos de Entrenamiento en Problemas de Clasificaci{\'o}n con Clases no Balanceadas y {\'A}rboles de Decisi{\'o}n},
	booktitle = {Proceedings of VI Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {183-190},
	address = {M{\'a}laga (Spain)},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and F. Herrera}
}
@conference {4484560,
	title = {A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers},
	booktitle = {2008 3rd International Workshop on Genetic and Evolving Systems},
	year = {2008},
	month = {March},
	pages = {11-16},
	keywords = {Bagging, bagging fuzzy rule-based classification system, Boosting, component classifier, Design methodology, evolutionary computation, fuzzy set theory, Fuzzy systems, genetic algorithms, heuristic fuzzy classification rule generation method, Humans, knowledge based systems, learning (artificial intelligence), machine learning, multicriteria genetic algorithm, pattern classification, Proposals, Scalability},
	doi = {10.1109/GEFS.2008.4484560},
	author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@article {simidat57,
	title = {A memetic algorithm for Evolutionary Prototype Selection: A Scaling Up Approach},
	journal = {Pattern Recognition},
	volume = {41},
	number = {8},
	year = {2008},
	pages = {2693-2709},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@conference {inproceedings,
	title = {A Minimum Risk Wrapper Algorithm for Genetically Selecting Imprecisely Observed Features, Applied to the Early Diagnosis of Dyslexia},
	booktitle = {Lecture Notes in Computer Science - LNCS},
	volume = {5271},
	year = {2008},
	month = {09},
	pages = {608-615},
	doi = {10.1007/978-3-540-87656-4_75},
	author = {S{\'a}nchez, Luciano and Palacios, Ana and Couso, In{\'e}s}
}
@conference {simidat43,
	title = {A Novel Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for High Dimensional Problems},
	booktitle = {3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)},
	year = {2008},
	pages = {101-106},
	address = {WittenBommerholz (Germany)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@article {simidat85,
	title = {A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets},
	journal = {Fuzzy Sets and Systems},
	volume = {159},
	number = {18},
	year = {2008},
	pages = {2378-2398},
	issn = {0165-0114},
	doi = {10.1016/j.fss.2007.12.023},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@conference {simidat66,
	title = {A Symbiotic CHC Co-evolutionary Algorithm for Automatic RBF Neural Networks Design},
	booktitle = {DCAI 2008: International Symposium on Distributed Computing and Artificial Intelligence},
	volume = {50/2009},
	year = {2008},
	month = {Salamanca},
	pages = {663-671},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus and Juan J. Merelo}
}
@conference {simidat111,
	title = {An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy System},
	booktitle = {Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	year = {2008},
	month = {June},
	pages = {1121-1128},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {4626756,
	title = {An Study on Data Mining Methods for Short-Term Forecasting of the Extra Virgin Olive Oil Price in the Spanish Market},
	booktitle = {2008 Eighth International Conference on Hybrid Intelligent Systems},
	year = {2008},
	month = {Sep.},
	pages = {943-946},
	abstract = {This paper presents the adaptation of an evolutionary cooperative competitive RBFN learning algorithm, CO2RBFN, for short-term forecasting of extra virgin olive oil price. The olive oil time series has been analyzed with a new evolutionary proposal for the design of RBFNs, CO2RBFN. Results obtained has been compared with ARIMA models and other data mining methods such as a fuzzy system developed with a GA-P algorithm, a multilayer perceptron trained with a conjugate gradient algorithm and a radial basis function network trained with a LMS algorithm. The experimentation shows the high efficacy reached for the applied methods, specially for data mining methods which have slightly outperformed ARIMA methodology.},
	keywords = {Algorithm design and analysis, ARIMA, ARIMA models, Artificial neural networks, autoregressive moving average processes, CO2RBFN, conjugate gradient algorithm, conjugate gradient methods, data mining, data mining methods, evolutionary cooperative competitive, extra virgin olive oil price, Forecasting, fuzzy system, Fuzzy systems, GA-P algorithm, genetic algorithms, least mean squares methods, LMS algorithm, multilayer perceptron, multilayer perceptrons, Olive Oil Price, olive oil time series, Petroleum, pricing, radial basis function network, radial basis function networks, RBFN learning algorithm, short-term forecasting, Spanish market, time series, Time series analysis, time series forecasting, Training, vegetable oils},
	doi = {10.1109/HIS.2008.132},
	author = {P. P{\'e}rez and M. P. Fr{\'\i}as and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. d. Jesus and M. Parras and F. J. Torres}
}
@conference {simidat51,
	title = {Aplicaci{\'o}n de un algoritmo de extracci{\'o}n de reglas difusas para miner{\'\i}a de uso web},
	booktitle = {XIV Congreso Espa{\~n}ol de Tecnolog{\'\i}as y L{\'o}gica Difusa (ESTYLF)},
	year = {2008},
	month = {September},
	address = {Langreo-Mieres (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and V. M. Rivas and M. J. del Jesus}
}
@conference {simidat53,
	title = {Applied Techniques of Engineering to Non-Structured Data Model},
	booktitle = {International Symposium on Distributed Computing and Artificial Intelligence (DCAI)},
	volume = {50},
	year = {2008},
	pages = {410-414},
	address = {Salamanca (Spain)},
	doi = {10.1007/978-3-540-85863-8_48},
	author = {C. J. Carmona and M. J. del Jesus and P. Guerrero and R. Pe{\~n}a-Santiago and V. M. Rivas}
}
@conference {simidat65,
	title = {Automatic Neural Net Design by Means of a Symbiotic Co-evolutionary Algorithm},
	booktitle = {HAIS 2008: 3rd International Workshop on Hybrid Artificial Intelligence System},
	series = {Lecture Notes in Computer Science},
	year = {2008},
	month = {October},
	pages = {140-147},
	publisher = {Springer},
	organization = {Springer},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus}
}
@conference {10.1007/978-3-540-85027-4_16,
	title = {Defuzzification of Fuzzy p-Values},
	booktitle = {Soft Methods for Handling Variability and Imprecision},
	year = {2008},
	pages = {126{\textendash}132},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {We provide a new description of the notion of fuzzy p-value, within the context of the theory of imprecise probabilities. The fuzzy p-value is viewed as a representation of a certain second-order possibility measure. According to Walley, any second-order possibility measure can be converted into a pair of lower and upper probabilities. Thus, we can convert the fuzzy p-value into an interval in the real line. We derive a construction of imprecise (but non fuzzy) tests, which are formally similar to recent tests used to manage with set-valued data.},
	isbn = {978-3-540-85027-4},
	author = {Couso, In{\'e}s and S{\'a}nchez, Luciano},
	editor = {Dubois, Didier and Lubiano, M. Asunci{\'o}n and Prade, Henri and Gil, Mar{\'\i}a {\'A}ngeles and Grzegorzewski, Przemys{\l}aw and Hryniewicz, Olgierd}
}
@conference {simidat82,
	title = {Estudio de la influencia de las medidas de complejidad de los datos en los Sistemas de Clasifcaci{\'o}n Basados en Reglas Difusas: An{\'a}lisis de la Raz{\'o}n Discriminante de Fisher},
	booktitle = {XIV Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2008},
	month = {September},
	pages = {257-263},
	address = {Mieres (Spain)},
	author = {J. Luengo and S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@article {article,
	title = {Evolutionary Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules},
	year = {2008},
	month = {01},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel}
}
@article {Otero2008,
	title = {Fuzzy-genetic optimization of the parameters of a low cost system for the optical measurement of several dimensions of vehicles},
	journal = {Soft Computing},
	volume = {12},
	number = {8},
	year = {2008},
	month = {Jun},
	pages = {751{\textendash}764},
	abstract = {When designing optical measurement systems, it is common to use cameras, lenses and frame grabbers specially designed for metrology applications. These devices are expensive, therefore optical metrology is not the technology of choice in low cost applications. On the contrary to this, surveillance video cameras and home oriented frame grabbers are cheap, but imprecise. Their use introduces inaccuracies in the measurements, that sometimes can be compensated by software. Following this last approach, in this paper it is proposed to use fuzzy techniques to exploit the tolerance for imprecision of a practical metrology application (to automate the measurement of vehicle dimensions in Technical Inspection of Vehicles in Spain, the equivalent of the Ministry Of Transport Test or MOT Test in UK) and to find an economic solution. It will be shown that a genetic algorithm (GA), guided by a fuzzy characterization of the sources of error, can optimize the placement of the video cameras in a station so that these mentioned sensors can be used to take measurements within the required tolerance.},
	issn = {1433-7479},
	doi = {10.1007/s00500-007-0234-3},
	url = {https://doi.org/10.1007/s00500-007-0234-3},
	author = {Otero, J. and S{\'a}nchez, L. and Alcal{\'a}-Fdez, J.}
}
@article {COUSO2008237,
	title = {Higher order models for fuzzy random variables},
	journal = {Fuzzy Sets and Systems},
	volume = {159},
	number = {3},
	year = {2008},
	note = {Theme: Probability and Statistics},
	pages = {237 - 258},
	abstract = {A fuzzy random variable is viewed as the imprecise observation of the outcomes in a random experiment. Since randomness and vagueness coexist in the same framework, it seems reasonable to integrate fuzzy random variables into imprecise probabilities theory. Nevertheless, fuzzy random variables are commonly presented in the literature as classical measurable functions associated to a classical probability measure. We present here a higher order possibility model that represents the imprecise information provided by a fuzzy random variable. We compare it with previous classical models in the literature. First, some aspects about the acceptability function associated to a fuzzy random variable are investigated. Secondly, we present three different higher order possibility models, all of them arising in a natural way. We investigate their similarities and differences, and observe that the first one (the fuzzy probability envelope) is the most informative. Finally we compare the fuzzy probability envelope with the (classical) probability measure induced by the fuzzy random variable. We conclude that the classical probability measure does not always contain all relevant information provided by a fuzzy random variable.},
	keywords = {Fuzzy random variable, Imprecise probabilities, Random set, Second order possibility measure},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/j.fss.2007.09.004},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011407003971},
	author = {In{\'e}s Couso and Luciano S{\'a}nchez}
}
@conference {simidat52,
	title = {Influencia de la granularidad y de las medidas de calidad en SDIGA},
	booktitle = {XIV Congreso Espa{\~n}ol de Tecnolog{\'\i}as y L{\'o}gica Difusa( ESTYLF)},
	year = {2008},
	note = {TIN2005-08386-C05-01,TIN2005-08386-C05-03},
	month = {September},
	address = {Langreo-Mieres (Spain)},
	author = {P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {simidat44,
	title = {KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems},
	booktitle = {3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)},
	year = {2008},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {83-88},
	address = {WittenBommerholz (Germany)},
	author = {J. Alcal{\'a}-Fdez and S. Garc{\'\i}a and F. J. Berlanga and A. Fern{\'a}ndez and L. S{\'a}nchez and M. J. del Jesus and F. Herrera}
}
@article {610,
	title = {Making CN1 -SD Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection},
	journal = {Expert System with Applications},
	volume = {35},
	number = {4},
	year = {2008},
	pages = {1949-1965},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel and Garc{\'\i}a, Salvador}
}
@article {simidat58,
	title = {Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection},
	journal = {Expert Systems with Applications},
	volume = {35},
	year = {2008},
	pages = {1949-1965},
	author = {J. R. Cano and F. Herrera and M. Lozano and S. Garc{\'\i}a}
}
@conference {simidat112,
	title = {Multi-Objective Genetic Fuzzy Systems: On the Necessity of Including Expert Knowledge in the MOEA Design Process},
	booktitle = {Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	year = {2008},
	month = {June},
	pages = {1446-1453},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {SANCHEZ2008607,
	title = {Mutual information-based feature selection and partition design in fuzzy rule-based classifiers from vague data},
	journal = {International Journal of Approximate Reasoning},
	volume = {49},
	number = {3},
	year = {2008},
	pages = {607 - 622},
	abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to vague datasets is arguable. In particular, when we select features for being used in fuzzy rule-based classifiers, we often use a mutual information-based ranking of the relevance of inputs. But, either with crisp or fuzzy data, fuzzy rule-based systems route the input through a fuzzification interface. The fuzzification process may alter this ranking, as the partition of the input data does not need to be optimal. In our opinion, to discover the most important variables for a fuzzy rule-based system, we want to compute the mutual information between the fuzzified variables, and we should not assume that the ranking between the crisp variables is the best one. In this paper we address these problems, and propose an extended definition of the mutual information between two fuzzified continuous variables. We also introduce a numerical algorithm for estimating the mutual information from a sample of vague data. We will show that this estimation can be included in a feature selection algorithm, and also that, in combination with a genetic optimization, the same definition can be used to obtain the most informative fuzzy partition for the data. Both applications will be exemplified with the help of some benchmark problems.},
	keywords = {feature selection, Fuzzy fitness, Genetic Fuzzy Systems, vague data},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/j.ijar.2008.06.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X08001102},
	author = {Luciano S{\'a}nchez and M. Rosario Su{\'a}rez and J.R. Villar and In{\'e}s Couso}
}
@article {SANCHEZ2008952,
	title = {Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms},
	journal = {Information Sciences},
	volume = {178},
	number = {4},
	year = {2008},
	pages = {952 - 970},
	abstract = {Transparent models search for a balance between interpretability and accuracy. This paper is about the estimation of transparent models of chaotic systems from data, which are accurate and simple enough for their expression to be understandable by a human expert. The models we propose are discrete, built upon common blocks in control engineering (gain, delay, sum, etc.) and optimized both in their complexity and accuracy. The accuracy of a discrete model can be measured by means of the average error between its prediction for the next sampling period and the true output at that time, or {\textquoteleft}one-step error{\textquoteright}. A perfect model has zero one-step error, but a small error is not always associated with an approximate model, especially in chaotic systems. In chaos, an arbitrarily low difference between two initial states will produce uncorrelated trajectories, thus a model with a low one-step error may be very different from the desired one. Even though a recursive evaluation (multi-step prediction) improves the fitting, in this work we will show that a learning algorithm may not converge to an appropriate model, unless we include some terms that depend on estimates of certain properties of the model (so called {\textquoteleft}invariants{\textquoteright} of the chaotic series). We will show this graphically, by means of the reconstructed attractors of the original system and the model. Therefore, we also propose to follow a multi-objective approach to model chaotic processes and to apply a simulated annealing-based optimization to obtain transparent models.},
	keywords = {Chaotic systems, Genetic programming, Multi-objective simulated annealing, Transparent models},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2007.09.029},
	url = {http://www.sciencedirect.com/science/article/pii/S002002550700463X},
	author = {Luciano S{\'a}nchez and Jos{\'e} R. Villar}
}
@conference {4626687,
	title = {On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles},
	booktitle = {2008 Eighth International Conference on Hybrid Intelligent Systems},
	year = {2008},
	month = {Sep.},
	pages = {549-554},
	abstract = {In this contribution we explore the combination of bagging with random subspace and two variants of Battiti{\textquoteright}s mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.},
	keywords = {Bagging, classification, Classification algorithms, fuzzy rule-based classification ensembles, fuzzy set theory, Gallium, genetic algorithms, Glass, multicriteria genetic algorithms, mutual information-based feature selection, Sonar, Training, Vehicles},
	doi = {10.1109/HIS.2008.147},
	author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@inbook {simidat110,
	title = {On the use of Multiobjective Genetic Algorithms to Improve the Accuracy-Interpretability Trade-Off of Fuzzy Rule-Based Systems},
	booktitle = {Multi-objective Evolutionary Algorithms for Knowledge Discovery from Data Bases},
	volume = {98},
	year = {2008},
	isbn = {978-3-540-77466-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {A. Ghosh and S. Dehuri and S. Ghosh}
}
@inbook {Alcal{\'a}2008,
	title = {On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection},
	booktitle = {Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases},
	year = {2008},
	pages = {91{\textendash}107},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In the last years, multi-objective genetic algorithms have been successfully applied to obtain Fuzzy Rule-Based Systems satisfying different objectives, usually different performance measures.},
	isbn = {978-3-540-77467-9},
	doi = {10.1007/978-3-540-77467-9_5},
	url = {https://doi.org/10.1007/978-3-540-77467-9_5},
	author = {Alcal{\'a}, R. and Alcal{\'a}-Fdez, J. and M. J. Gacto and F. Herrera},
	editor = {Ghosh, Ashish and Dehuri, Satchidananda and Ghosh, Susmita}
}
@conference {simidat63,
	title = {Parameter Estimation for Radial Basis Function Neural Network Design by Means of Two Symbiotic Algorithms},
	booktitle = {Proceedings of the 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP)},
	year = {2008},
	month = {September},
	pages = {164-169},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = {Valencia (Spain)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus and Juan J. Merelo}
}
@article {LozanoHC08,
	title = {Replacement strategies to preserve useful diversity in steady-state genetic algorithms},
	journal = {Information Sciences},
	volume = {178},
	number = {23},
	year = {2008},
	pages = {4421{\textendash}4433},
	doi = {10.1016/j.ins.2008.07.031},
	author = {M. Lozano and J. R. Cano and F. Herrera}
}
@conference {article,
	title = {Sistemas Basados en Reglas Difusas en Clasificaci{\'o}n: Nuevos Retos},
	booktitle = {XIV Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy},
	year = {2008},
	month = {09},
	address = {Cuencas Mineras Asturianas (Espa{\~n}a)},
	author = {M. J. del Jesus}
}
@conference {simidat62,
	title = {Study of the Robustness of a Meta-Algorithm for the Estimation of Parameters in Artificial Neural Networks Design},
	booktitle = {Proceedings of the 2008 Eighth International Conference on Hybrid Intelligent Systems (HIS)},
	year = {2008},
	month = {September},
	pages = {519-524},
	address = {Barcelona (Spain)},
	author = {E. Parras-Guti{\'e}rrez and M. J. del Jesus and V. M. Rivas and Juan J. Merelo}
}
@article {simidat87,
	title = {Subgroup Discovery in Large Size Data Sets Preprocessed Using Stratified Instance Selection for Increasing the Presence of Minority Classes},
	journal = {Pattern Recognition Letters},
	volume = {29},
	year = {2008},
	pages = {2156-2164},
	doi = {10.1016/j.patrec.2008.08.001},
	author = {J. R. Cano and S. Garc{\'\i}a and F. Herrera}
}
@inbook {Jesus2008,
	title = {Subgroup Discovery with Linguistic Rules},
	booktitle = {Fuzzy Sets and Their Extensions: Representation, Aggregation and Models},
	year = {2008},
	pages = {411{\textendash}430},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest. Subgroup discovery algorithms aim at discovering individual rules, which must be represented in explicit symbolic form and which must be simple and understandable in order to be recognized as actionable by potential users.},
	isbn = {978-3-540-73723-0},
	doi = {10.1007/978-3-540-73723-0_21},
	url = {https://doi.org/10.1007/978-3-540-73723-0_21},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera},
	editor = {Bustince, Humberto and F. Herrera and Montero, Javier}
}
@conference {article,
	title = {Utilizaci{\'o}n de un sistema basado en reglas difusas para la aplicaci{\'o}n de operadores en un algoritmo cooperativo-competitivo},
	booktitle = {XIV Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y logica fuzzy},
	year = {2008},
	month = {09},
	address = {Cuencas Mineras Asturianas},
	author = {M.D. P{\'e}rez-Godoy and Rivera Rivas, Antonio and M Jos{\'e}, Rivas and D{\'\i}az, Jesus and Jos{\'e}, F and Rivera, Berlanga}
}
@conference {simidat108,
	title = {A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1367-1372},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto}
}
@article {simidat97,
	title = {A Multi-objectiveGenetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {15},
	number = {5},
	year = {2007},
	pages = {539{\textendash}557},
	doi = {10.1142/S0218488507004868},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera and J. Alcal{\'a}-Fdez}
}
@article {Rivera2007,
	title = {A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks},
	journal = {Soft Computing},
	volume = {11},
	number = {7},
	year = {2007},
	month = {May},
	pages = {655{\textendash}668},
	abstract = {This paper presents a new multiobjective cooperative{\textendash}coevolutive hybrid algorithm for the design of a Radial Basis Function Network (RBFN). This approach codifies a population of Radial Basis Functions (RBFs) (hidden neurons), which evolve by means of cooperation and competition to obtain a compact and accurate RBFN. To evaluate the significance of a given RBF in the whole network, three factors have been proposed: the basis function{\textquoteright}s contribution to the network{\textquoteright}s output, the error produced in the basis function radius, and the overlapping among RBFs. To achieve an RBFN composed of RBFs with proper values for these quality factors our algorithm follows a multiobjective approach in the selection process. In the design process, a Fuzzy Rule Based System (FRBS) is used to determine the possibility of applying operators to a certain RBF. As the time required by our evolutionary algorithm to converge is relatively small, it is possible to get a further improvement of the solution found by using a local minimization algorithm (for example, the Levenberg{\textendash}Marquardt method). In this paper the results of applying our methodology to function approximation and time series prediction problems are also presented and compared with other alternatives proposed in the bibliography.},
	issn = {1432-7643},
	doi = {10.1007/s00500-006-0128-9},
	url = {https://doi.org/10.1007/s00500-006-0128-9},
	author = {A.J. Rivera-Rivas and Rojas, I. and Ortega, J. and M. J. del Jesus}
}
@conference {Martnez2007ASA,
	title = {A simple and fast hardware-accelerated point-in-polygon test},
	booktitle = {GRAPP},
	year = {2007},
	author = {Francisco Mart{\'\i}nez and Antonio J. Rueda Ruiz and Francisco R. Feito-Higueruela}
}
@conference {simidat74,
	title = {A study on the Use of the Fuzzy Reasoning Method based on the Winning Rule Vs. Voting Procedure for Classification with Imbalanced Data Sets},
	booktitle = {Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN)},
	series = {Lecture Notes on Computer Science 4507},
	year = {2007},
	month = {June},
	pages = {375-382},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {San Sebasti{\'a}n (Spain)},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@article {4286977,
	title = {Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {15},
	number = {4},
	year = {2007},
	month = {Aug},
	pages = {551-562},
	abstract = {In our opinion, and in accordance with current literature, the precise contribution of genetic fuzzy systems to the corpus of the machine learning theory has not been clearly stated yet. In particular, we question the existence of a set of problems for which the use of fuzzy rules, in combination with genetic algorithms, produces more robust models, or classifiers that are inherently better than those arising from the Bayesian point of view. We will show that this set of problems actually exists, and comprises interval and fuzzy valued datasets, but it is not being exploited. Current genetic fuzzy classifiers deal with crisp classification problems, where the role of fuzzy sets is reduced to give a parametric definition of a set of discriminant functions, with a convenient linguistic interpretation. Provided that the customary use of fuzzy sets in statistics is vague data, we propose to test genetic fuzzy classifiers over imprecisely measured data and design experiments well suited to these problems. The same can be said about genetic fuzzy models: the use of a scalar fitness function assumes crisp data, where fuzzy models, a priori, do not have advantages over statistical regression.},
	keywords = {Bayesian methods, data handling, Design for experiments, discriminant function, fuzzy fitness function, fuzzy rule-based classifiers, fuzzy rule-based models, fuzzy rules, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithm, genetic algorithms, genetic fuzzy classifiers, Genetic Fuzzy Systems, learning (artificial intelligence), linguistic interpretation, machine learning, machine learning theory, Parametric statistics, pattern classification, Robustness, scalar fitness function, statistical analysis, Stochastic resonance, vague data},
	issn = {1063-6706},
	doi = {10.1109/TFUZZ.2007.895942},
	author = {L. S{\'a}nchez and I. Couso}
}
@conference {simidat75,
	title = {An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets},
	booktitle = {International Workshop on Fuzzy Logic and Applications (WILF)},
	series = {Lecture Notes in Computer Science 4578},
	year = {2007},
	month = {July},
	pages = {170-179},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Genova (Italy)},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@conference {simidat54,
	title = {An{\'a}lisis of Evolutionary Prototype Selection by means of a Data Complexity Measure based on Class Separabilty},
	booktitle = {Actas del Taller de Miner{\'\i}a de Datos y Aprendizaje (TAMIDA)},
	year = {2007},
	pages = {145-152},
	address = {Zaragoza},
	author = {J. R. Cano and S. Garc{\'\i}a and F. Herrera and E. Bernad{\'o}-Mansilla}
}
@conference {simidat40,
	title = {Aplicaci{\'o}n de Algoritmos Evolutivos de Descubrimiento de Subgrupos en e-Learning: un Caso de Estudio},
	booktitle = {V Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2007},
	pages = {493-500},
	address = {Tenerife (Spain)},
	author = {C. Romero and P. Gonz{\'a}lez and S. Ventura and M. J. del Jesus and F. Herrera}
}
@conference {simidat109,
	title = {Aprendizaje Evolutivo de los Contextos de las Funciones de Pertenencia para Extraer Reglas de Asociaci{\'o}n Difusas},
	booktitle = {Proceedings of the II Congreso Espa{\~n}ol de Inform{\'a}tica (CEDI 2007). II Simposio sobre L{\'o}gica Fuzzy y Soft Computing (LFSC)},
	year = {2007},
	month = {September},
	pages = {25-32},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@article {Snchez2007BoostingFR,
	title = {Boosting fuzzy rules in classification problems under single-winner inference},
	journal = {Int. J. Intell. Syst.},
	volume = {22},
	year = {2007},
	pages = {1021-1034},
	author = {Luciano S{\'a}nchez and Jos{\'e} Otero}
}
@conference {inproceedings,
	title = {CoEvRBFN: An Approach to Solving the Classification Problem with a Hybrid Cooperative-Coevolutive Algorithm},
	year = {2007},
	month = {06},
	pages = {324-332},
	doi = {10.1007/978-3-540-73007-1_40},
	author = {M.D. P{\'e}rez-Godoy and Rivera Rivas, Antonio and M. J. del Jesus and Rojas, Ignacio}
}
@article {article,
	title = {Disenos experimentales y tests estadisticos, tendencias actuales en Machine Learning.},
	year = {2007},
	month = {01},
	author = {Otero, J and S{\'a}nchez, Luciano}
}
@article {simidat41,
	title = {Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {15},
	number = {4},
	year = {2007},
	pages = {578-592},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@article {simidat56,
	title = {Evolutionary Stratified Training Set Selection for Extracting Classification Rules with trade off Precision-Interpretability},
	journal = {Data \& Knowledge Engineering},
	volume = {60},
	number = {1},
	year = {2007},
	pages = {90-108},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {Martinez2007,
	title = {Experimentando con la paralelizaci{\'o}n de un problema de c{\'a}lculo utilizando hardware gr{\'a}fico},
	booktitle = {XVIII Jornadas de Paralelismo},
	year = {2007},
	pages = {791-798},
	address = {Zaragoza, del 11 al 14 de Septiembre},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda-Ruiz and Francisco Ram{\'o}n Feito-Higueruela and Mar{\'\i}a Pilar Fr{\'\i}as-Bustamante}
}
@conference {simidat107,
	title = {Genetic Learning of Membership Functions for Mining Fuzzy Association Rules},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1538-1543},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@inbook {687,
	title = {LA DESCOMPOSICI{\'O}N MULTI-L-REP DE POLIEDROS},
	booktitle = {Hacad: Herramientas avanzadas en CAD},
	year = {2007},
	pages = {207-222},
	publisher = {Robert Joan Arinyo, Juan Carlos Torres Cantero, Francisco Ramon Feito Higueruela (EDS)},
	organization = {Robert Joan Arinyo, Juan Carlos Torres Cantero, Francisco Ramon Feito Higueruela (EDS)},
	address = {Ja{\'e}n (Espa{\~n}a)},
	issn = {978-84-690-6855-7},
	author = {Joan-Arinyo, Robert and Torres-Cantero, Juan Carlos and Feito, Francisco Ram{\'o}n}
}
@conference {Martinez2007,
	title = {La descomposici{\'o}n multi-L-REP de s{\'o}lidos y su aplicaci{\'o}n a un test de inclusi{\'o}n},
	booktitle = {XVII Congreso Espa{\~n}ol de Inform{\'a}tica Gr{\'a}fica},
	year = {2007},
	pages = {159-168},
	address = {Zaragoza, del 11 al 14 de Septiembre},
	isbn = {978-84-9732-595-0},
	author = {Francisco Mart{\'\i}nez and Antonio Jes{\'u}s Rueda-Ruiz}
}
@conference {4295659,
	title = {Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.},
	keywords = {Degradation, Fuzzy sets, Fuzzy systems, genetic algorithms, Global Positioning System, incremental rule base learning techniques, Iterative algorithms, iterative learning degrades, knowledge based systems, learning (artificial intelligence), learning fuzzy linguistic models, Low Quality Data, Noise measurement, Pareto front form, Pareto optimisation, Position measurement, Stochastic resonance, Uncertainty},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295659},
	author = {L. S{\'a}nchez and J. Otero}
}
@conference {4222979,
	title = {Modeling Vague Data with Genetic Fuzzy Systems under a Combination of Crisp and Imprecise Criteria},
	booktitle = {2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making},
	year = {2007},
	month = {April},
	pages = {30-37},
	abstract = {Multicriteria genetic algorithms can produce fuzzy models with a good balance between their precision and their complexity. The accuracy of a model is usually measured by the mean squared error of its residual. When vague training data is used, the residual becomes a fuzzy number, and it is needed to optimize a combination of crisp and fuzzy objectives in order to learn balanced models. In this paper, we will extend the NSGA-II algorithm to this last case, and test it over a practical problem of causal modeling in marketing. Different setups of this algorithm are compared, and it is shown that the algorithm proposed here is able to improve the generalization properties of those models obtained from the defuzzified training data.},
	keywords = {Additive noise, combination, Computer science, crisp objectives, defuzzified training data, fuzzy logic, fuzzy models, fuzzy objectives, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, Genetic Fuzzy Systems, Global Positioning System, mean squared error, multicriteria genetic algorithms, Noise measurement, NSGA-II algorithm, Position measurement, Probability distribution, Stochastic resonance, Training data, vague data modeling},
	doi = {10.1109/MCDM.2007.369413},
	author = {L. S{\'a}nchez and I. Couso and J. Casillas}
}
@conference {simidat49,
	title = {Multiobjective genetic algorithm for extractiong subgroup discovery fuzzy rules},
	booktitle = {2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (IEEE MCDM)},
	year = {2007},
	pages = {50-57},
	publisher = {Omnipress},
	organization = {Omnipress},
	address = {Honolulu (USA)},
	author = {P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@conference {4295638,
	title = {Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {In the design of fuzzy rule-based classification systems (FRBCSs) a feature selection process which determines the most relevant features is a crucial component in the majority of the classification problems. This simplification process increases the efficiency of the design process, improves the interpretability of the FRBCS obtained and its generalization capacity. Most of the feature selection algorithms provide a set of variables which are adequate for the induction process according to different quality measures. Nevertheless it can be useful for the induction process to determine not only a set of variables but also different set of variables. These sets of variables can be used for the design of a set of FRBCSs which can be combined in a multiclassifler system, improving the prediction capacity increasing its description capacity. In this work, different proposals of niching genetic algorithms for the feature selection process are analyzed. The different sets of features provided by them are used in a multiclassifier system designed by means of a genetic proposal. The experimentation shows the adaptation of this type of genetic algorithms to the FRBCS design.},
	keywords = {Algorithm design and analysis, classification, data mining, Databases, description capacity, Feature extraction, feature selection algorithms, Fuzzy reasoning, fuzzy rule-based classification systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithms, induction process, Knowledge representation, multiclassifler system, niching genetic algorithms, prediction capacity, Process design, Proposals},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295638},
	author = {Jos{\'e} Aguilera and M. Chica and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Preface: Special Issue on Genetic Fuzzy Systems and the Interpretability{\textendash}Accuracy Trade-off},
	journal = {International Journal of Approximate Reasoning},
	volume = {44},
	year = {2007},
	month = {01},
	pages = {1-3},
	doi = {10.1016/j.ijar.2006.06.002},
	author = {Casillas, Jorge and F. Herrera and P{\'e}rez, Ra{\'u}l and Villar, P}
}
@article {simidat96,
	title = {Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation},
	journal = {Soft Computing},
	volume = {11},
	number = {5},
	year = {2007},
	pages = {401-419},
	doi = {10.1007/s00500-006-0106-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {inproceedings,
	title = {Selecting an Appropriate Statistical Test for Comparing Multiple Experiments in Evolutionary Machine Learning},
	year = {2007},
	month = {09},
	author = {Otero, Jos{\'e} and S{\'a}nchez, Luciano and Alcala-Fdez, Jesus}
}
@conference {simidat115,
	title = {Servicios de informaci{\'o}n sobre la EPSJ basados en entornos de publicaci{\'o}n},
	booktitle = {Free Libre Open Source Systems International Conference},
	year = {2007},
	month = {March},
	pages = {67-82},
	address = {Jerez de la Frontera (C{\'a}diz)},
	author = {E. Parras-Guti{\'e}rrez and V. M. Rivas and M. J. del Jesus}
}
@conference {4295665,
	title = {Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied, partly because we lack numerical tools to quantify the interdependency between fuzzy random variables. In particular, many filter-type feature selection algorithms rely on crisp discretizations for estimating the mutual information between continuous variables, effectively preventing the use of vague data. Fuzzy rule based systems pass continuous input variables, in turn, through their own fuzzification interface. In the context of feature selection, should we rank the relevance of the inputs by means of their mutual information, it might happen that an apparently informative variable is useless after having been codified as a fuzzy subset of our catalog of linguistic terms. In this paper we propose to address both problems by estimating the mutual information with the same set of fuzzy partitions that will be used to codify the antecedents of the fuzzy rules. That is to say, we introduce a numerical algorithm for estimating the mutual information between two fuzzified continuous variables. This algorithm can be included in certain feature selection algorithms, and can also be used to obtain the most informative fuzzy partition for the data. The use of our definition will be exemplified with the help of some benchmark problems.},
	keywords = {codification, computational linguistics, Data preprocessing, Feature extraction, feature selection, fuzzification interface, fuzzy discretization, fuzzy random variables, fuzzy rule based systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, Information filtering, Information filters, knowledge based systems, linguistic terms, mutual information, Partitioning algorithms, Random variables, Spatial databases, vague data},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295665},
	author = {L. S{\'a}nchez and M. R. Suarez and J. R. Villar and I. Couso}
}
@article {757,
	title = {Special issue on genetic fuzzy systems and the interpretability-accuracy trade-off},
	journal = {International Journal of Approximate Reasoning},
	volume = {44},
	year = {2007},
	pages = {1-3},
	issn = {0888-613X},
	doi = {10.1016/J.IJAR.2006.06.002},
	author = {J. Casillas and F. Herrera and F.G.R. P{\'e}rez and M. J. del Jesus and P. Villar}
}
@conference {simidat77,
	title = {Statistical Comparisons by Means of Non-Parametric Tests: A Case Study on Genetic Based Machine Learning},
	booktitle = {Proceedings of the II Congreso Espa{\~n}ol de Inform{\'a}tica (CEDI 2007). V Taller Nacional de Miner{\'\i}a de Datos y Aprendizaje (TAMIDA)},
	year = {2007},
	month = {September},
	pages = {95-104},
	address = {Zaragoza (Spain)},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and A.D. Ben{\'\i}tez and F. Herrera}
}
@conference {simidat55,
	title = {Un algoritmo mem{\'e}tico para la selecci{\'o}n de prototipos: Una propuesta eficiente para problemas de tama{\~n}o medio},
	booktitle = {Proceedings Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2007},
	address = {Tenerife},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@conference {inproceedings,
	title = {Un estudio sobre el uso de algoritmos gen{\'e}ticos multimodales para selecci{\'o}n de caracter{\'\i}sticas},
	year = {2007},
	month = {02},
	author = {Jos{\'e} Aguilera and , ~J.~J and Chica, Manuel and M. J. del Jesus and , ~M.~J and F. Herrera and , ~F}
}
@conference {simidat70,
	title = {A first study on the use of fuzzy rule based classification systems for problems with imbalanced data sets},
	booktitle = {Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS)},
	year = {2006},
	month = {September},
	pages = {63-72},
	address = {Magdeburg (Germany)},
	author = {M. J. del Jesus and A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera}
}
@conference {simidat37,
	title = {A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems},
	booktitle = {The Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC)},
	series = {LNCS},
	volume = {4029},
	year = {2006},
	pages = {182-191},
	publisher = {Springer},
	organization = {Springer},
	address = {Zakopane (Poland)},
	author = {F. J. Berlanga and M. J. del Jesus and M. J. Gacto and F. Herrera}
}
@conference {4016720,
	title = {A Multiobjective Genetic Fuzzy System with Imprecise Probability Fitness for Vague Data},
	booktitle = {2006 International Symposium on Evolving Fuzzy Systems},
	year = {2006},
	month = {Sep.},
	pages = {131-136},
	abstract = {When questionnaires are designed, each factor under study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we represent the information contained in such a set of values by means of a fuzzy number. A fuzzy statistics-based interpretation of the semantic of a fuzzy set is used for this purpose, as we consider that this fuzzy number is a nested family of confidence intervals for the value of the variable. The accuracy of the model is expressed by means of an interval-valued function, derived from a definition of the variance of a fuzzy random variable. A multicriteria genetic learning algorithm, able to optimize this interval-valued function, is proposed. As an example of the application of this algorithm, a practical problem of modeling in marketing is solved},
	keywords = {Computer errors, Computer science, Consumer behavior, fuzzy random variable variance, fuzzy set, fuzzy set theory, Fuzzy sets, fuzzy statistics-based interpretation, Fuzzy systems, genetic algorithms, Genetics, imprecise probability fitness, interval-valued function, learning (artificial intelligence), multicriteria genetic learning algorithm, multiobjective genetic fuzzy system, probability, Random variables, Statistics, vague data},
	doi = {10.1109/ISEFS.2006.251156},
	author = {L. S{\'a}nchez and I. Couso and J. Casillas}
}
@conference {simidat34,
	title = {A proposal of Evolutionary Prototype Selection for Class Imbalance Problems},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)},
	series = {LNCS},
	volume = {4224},
	year = {2006},
	pages = {1415-1423},
	author = {S. Garc{\'\i}a and J. R. Cano and A. Fern{\'a}ndez and F. Herrera}
}
@conference {article,
	title = {Boosting of fuzzy models for high-dimensional imprecise datasets},
	booktitle = {Information Processing and Management of Uncertainty in Knowledge-Based Systems},
	year = {2006},
	month = {07},
	address = {Par{\'\i}s (Francia)},
	author = {S{\'a}nchez, Luciano and Ram{\'o}n, Jos{\'e}}
}
@inbook {simidat104,
	title = {Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System},
	booktitle = {Fuzzy Applications in Industrial Engineering, Studies in Fuzziness and Soft Computing},
	volume = {201},
	year = {2006},
	pages = {89-117},
	isbn = {978-3-540-33516-0},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {Cengiz Kahraman}
}
@conference {Villar:2006:GAE:2176301.2176330,
	title = {Genetic Algorithms for Estimating Longest Path from Inherently Fuzzy Data Acquired with GPS},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning},
	series = {IDEAL{\textquoteright}06},
	year = {2006},
	pages = {232{\textendash}240},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin, Heidelberg},
	isbn = {3-540-45485-3, 978-3-540-45485-4},
	doi = {10.1007/11875581_28},
	url = {http://dx.doi.org/10.1007/11875581_28},
	author = {Villar, Jos{\'e} and Otero, Adolfo and Otero, Jos{\'e} and S{\'a}nchez, Luciano}
}
@conference {simidat38,
	title = {Genetic Lateral and Amplitude Tuning with Rule Selection for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems},
	booktitle = {19th International Conference on Industrial \& Engineering Applications of Artificial Intelligence \& Expert Systems (IEA/AIE)},
	series = {LNCS},
	volume = {4031},
	year = {2006},
	pages = {452-461},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Annecy (France)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat39,
	title = {Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection},
	booktitle = {Modeling Decisions for Artificial Intelligence (MDAI)},
	series = {LNCS},
	volume = {3885},
	year = {2006},
	pages = {317-328},
	publisher = {Springer},
	organization = {Springer},
	address = {Tarragona (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat33,
	title = {Incorporating Knowledge in Evolutionary Prototype Selection},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)},
	series = {LNCS},
	volume = {4224},
	year = {2006},
	pages = {1358-1366},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@article {Otero2006,
	title = {Induction of descriptive fuzzy classifiers with the Logitboost algorithm},
	journal = {Soft Computing},
	volume = {10},
	number = {9},
	year = {2006},
	month = {Jul},
	pages = {825{\textendash}835},
	abstract = {Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or {\textquoteleft}{\textquoteleft}Logitboost{\textquoteleft}{\textquoteleft}. The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to learn fuzzy rules in classification problems.},
	issn = {1433-7479},
	doi = {10.1007/s00500-005-0011-0},
	url = {https://doi.org/10.1007/s00500-005-0011-0},
	author = {Otero, Jos{\'e} and S{\'a}nchez, Luciano}
}
@conference {1681710,
	title = {Knowledge Extraction from Fuzzy Data for Estimating Consumer Behavior Models},
	booktitle = {2006 IEEE International Conference on Fuzzy Systems},
	year = {2006},
	month = {July},
	pages = {164-170},
	abstract = {For certain problems of casual modeling in marketing, the information is obtained by means of questionnaires. When these questionnaires include more than one item for each observable variable, the value of this variable can not be assigned a number, but a potentially scattered set of values. In this paper, we propose to represent the information contained in this set of values by means of a fuzzy number. A novel fuzzy statistics-based interpretation of the semantic of a fuzzy set will be used for this purpose, as we will consider that this fuzzy number is a nested family of confidence intervals for a central tendency measure of the value of the variable. A genetic learning algorithm, able to extract association fuzzy rules from this data, is also proposed. The accuracy of the model will be expressed by means of a fuzzy-valued function. We propose to jointly minimize this function and the complexity of the rule based model with multicriteria genetic algorithms, that in turn will depend on a fuzzy ranking-based ordering of individuals.},
	keywords = {Artificial intelligence, association fuzzy rule extraction, casual modeling, Computer science, Computer science education, Consumer behavior, consumer behavior model, consumer behaviour, data mining, fuzzy data, fuzzy logic, fuzzy number, fuzzy ranking, fuzzy set theory, Fuzzy sets, fuzzy statistics, genetic algorithms, knowledge extraction, learning (artificial intelligence), learning algorithm, marketing data processing, multicriteria genetic algorithm, Scattering, semantic interpretation, statistical analysis},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2006.1681710},
	author = {J. Casillas and L. S{\'a}nchez}
}
@conference {simidat36,
	title = {Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing},
	booktitle = {6th Industrial Conference on Data Mining (ICDM)},
	series = {LNCS},
	volume = {4065},
	year = {2006},
	pages = {337-349},
	publisher = {Springer},
	organization = {Springer},
	address = {Leipzig (Germany)},
	author = {F. J. Berlanga and M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@conference {simidat105,
	title = {Obtaining Compact and Still Accurate Linguistic Fuzzy Rule-Based Systems by Using Multi-Objetive Genetic Algorithms},
	booktitle = {Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS)},
	year = {2006},
	pages = {53-62},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat106,
	title = {Obtenci{\'o}n de Sistemas Basados en Reglas Difusas Precisos y Compactos Mediante Algoritmos Gen{\'e}ticos Multiobjetivo},
	booktitle = {Proceedings of the XIII Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2006},
	pages = {1-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto}
}
@article {simidat35,
	title = {On the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining},
	journal = {Applied Soft Computing},
	volume = {6},
	year = {2006},
	pages = {323-332},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@article {article,
	title = {The multi-L-REP decomposition and its application to a point-in-polygon inclusion test},
	journal = {Computers \& Graphics},
	volume = {30},
	year = {2006},
	month = {12},
	pages = {947-958},
	doi = {10.1016/j.cag.2006.08.015},
	author = {Francisco Mart{\'\i}nez and Rueda, Antonio and Feito, Francisco}
}
@conference {simidat71,
	title = {Un primer estudio sobre el uso de los sistemas de clasificaci{\'o}n basados en reglas difusas en problemas de clasificaci{\'o}n con clases no balanceadas},
	booktitle = {Proceedings of the XIII Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2006},
	month = {September},
	pages = {89-95},
	address = {Ciudad Real (Spain)},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera and M. J. del Jesus}
}
@conference {article,
	title = {Un primer estudio sobre el uso de los sistemas de clasificaci{\'o}n basados en reglas difusas en problemas de clasificaci{\'o}n con clases no balanceadas},
	booktitle = {XIV Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy},
	year = {2006},
	month = {01},
	address = {Ciudad Real (Espa{\~n}ol)},
	author = {Fern{\'a}ndez, Alberto and Garc{\'\i}a, Salvador and F. Herrera and M. J. del Jesus}
}
@conference {article,
	title = {A fuzzy definition of mutual information with application to the design of genetic fuzzy classifiers},
	booktitle = {International Conference on Machine Intelligencea: ACIDCA-ICMI },
	year = {2005},
	month = {01},
	address = {Tunez},
	author = {S{\'a}nchez, Luciano and Su{\'a}rez, Mar{\'\i}a and Couso, In{\'e}s}
}
@inbook {simidat26,
	title = {A Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining},
	booktitle = {Soft Computing: Methodologies and Applications},
	year = {2005},
	pages = {271-284},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {J. R. Cano and F. Herrera and M. Lozano},
	editor = {F. Hoffmann and M. K{\"o}ppen and F. Klawonn and R. Roy}
}
@conference {simidat102,
	title = {Ajuste Evolutivo Lateral y de Amplitud de etiquetas para Sistemas Basados en Reglas Difusas},
	booktitle = {Simposio de Inteligencia Computacional (SICO)},
	year = {2005},
	pages = {481-488},
	address = {Granada (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {inproceedings,
	title = {Application of ANOVA to a Cooperative-Coevolutionary Optimization of RBFNs},
	booktitle = {Lecture Notes in Computer Science},
	volume = {3512},
	year = {2005},
	month = {06},
	pages = {297-305},
	doi = {10.1007/11494669_37},
	author = {Rivera Rivas, Antonio and Rojas, Ignacio and Ortega, Julio}
}
@conference {simidat30,
	title = {Aprendizaje de reglas difusas mediante programaci{\'o}n gen{\'e}tica en problemas con alta dimensionalidad},
	booktitle = {I Simposio sobre L{\'o}gica Fuzzy y Soft Computing (LFSC)},
	year = {2005},
	pages = {93-100},
	address = {Granada (Spain)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Assessing the differences in accuracy between GFSs with bootstrap tests},
	year = {2005},
	month = {01},
	author = {S{\'a}nchez, Luciano and Alcala-Fdez, Jesus}
}
@inbook {689,
	title = {De la teor{\'\i}a a la pr{\'a}ctica: una reflexi{\'o}n sobre el EEES en aula},
	booktitle = {Adaptaci{\'o}n del profesorado universitario al espacio europeo de educaci{\'o}n superior mediante el uso de nuevas tecnolog{\'\i}as},
	number = {69-77},
	year = {2005},
	publisher = {Grupo Pafpu Formapro de la UCUA},
	organization = {Grupo Pafpu Formapro de la UCUA},
	address = {Ja{\'e}n (Espa{\~n}a)},
	issn = {3-540-25726-8},
	author = {Romero, Samuel and J. R. Cano and Prados-Suarez, Mar{\'\i}a Belen and Rivero-Cejudo, Maria Linarejos}
}
@conference {simidat17,
	title = {Estudio de las Fases de un Algoritmo de Optimizaci{\'o}n para Redes de Funciones de Base Radial},
	booktitle = {Actas del Simposio de Inteligencia Computacional (SICO)},
	year = {2005},
	month = {September},
	pages = {237-244},
	address = {Granada. Spain},
	author = {A.J. Rivera-Rivas and I. Rojas and J. Ortega Lopera}
}
@inbook {simidat22,
	title = {Evolutionary Induction of Descriptive Rules in a Market Problem},
	booktitle = {Intelligent Data Mining. Techniques and Applications, Studies in Computational Intelligence},
	volume = {5},
	year = {2005},
	pages = {267-292},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero},
	editor = {D. Ruan and G. Chen and E.E. Kerre and G. Wets}
}
@conference {simidat103,
	title = {Genetic Lateral and Amplitude Tuning of Membership Functions for Fuzzy Systems},
	booktitle = {Proceedings of the 2nd International Conference on Machine Intelligence (ACIDCA-ICMI)},
	year = {2005},
	pages = {589-595},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat23,
	title = {Genetic tuning of fuzzy rule deep structures preserving interpretability for linguistic modeling},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {13},
	number = {1},
	year = {2005},
	pages = {13-29},
	author = {J. Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {simidat20,
	title = {Inducci{\'o}n evolutiva multiobjetivo de reglas de descripci{\'o}n de subgrupos en un problema de marketing},
	booktitle = {IV Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2005},
	pages = {661-669},
	address = {Granada (Spain)},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera}
}
@conference {simidat101,
	title = {IN-RECS: {\'I}ndice de Impacto de las Revistas Espa{\~n}olas de Ciencias Sociales},
	booktitle = {Proceedings of the IX Jornadas Espa{\~n}olas de Documentaci{\'o}n},
	year = {2005},
	author = {E. Jim{\'e}nez and E. Delgado and R. Ruiz-Ba{\~n}os and A.G. L{\'o}pez-Herrera and M. J. Gacto and D. Torres and M. Moneda and R. Ruiz-Ba{\~n}os and J.M. P{\'e}rez and R. Bail{\'o}n and E. Poyatos and M. J. Rodr{\'\i}guez}
}
@inbook {Cano2005,
	title = {Instance Selection Using Evolutionary Algorithms: An Experimental Study},
	booktitle = {Advanced Techniques in Knowledge Discovery and Data Mining},
	year = {2005},
	pages = {127{\textendash}152},
	publisher = {Springer London},
	organization = {Springer London},
	address = {London},
	abstract = {In this chapter, we carry out an empirical study of the performance of four representative evolutionary algorithm models considering two instance-selection perspectives, the prototype selection and the training set selection for data reduction in knowledge discovery. This study includes a comparison between these algorithms and other nonevolutionary instance-selection algorithms. The results show that the evolutionary instance-selection algorithms consistently outperform the nonevolutionary ones, offering two main advantages simultaneously, better instance-reduction rates and higher classification accuracy.},
	isbn = {978-1-84628-183-9},
	doi = {10.1007/1-84628-183-0_5},
	url = {https://doi.org/10.1007/1-84628-183-0_5},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel},
	editor = {Pal, Nikhil R. and Jain, Lakhmi}
}
@conference {article,
	title = {Layer-based Decompositions of Polyhedra},
	booktitle = {International Conference in Central Europe on Computer Graphics Visualization and Computer Vision},
	year = {2005},
	month = {January},
	author = {Rueda, Antonio and Francisco Mart{\'\i}nez and Feito, Francisco}
}
@conference {simidat29,
	title = {Learning compact fuzzy rule-based classification systems with genetic programming},
	booktitle = {4th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT)},
	year = {2005},
	pages = {1027-1032},
	address = {Barcelona (Spain)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@conference {simidat28,
	title = {Learning fuzzy rules using genetic programming: Context-free grammar definition for high-dimensionality problems},
	booktitle = {I International Workshop on Genetic Fuzzy Systems (GFS)},
	year = {2005},
	pages = {136-141},
	address = {Granada (Spain)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@conference {article,
	title = {Managing imprecisely observed data in Genetic Fuzzy Models by means of a fuzzy-valued fitness function},
	booktitle = {Congreso espa{\~n}ol de metaheur{\'\i}sticas, algoritmos evolutivos y bioinspirados.},
	year = {2005},
	month = {01},
	address = {Granada (Espa{\~n}a)},
	author = {S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@conference {simidat31,
	title = {Multiobjective evolutionary induction of subgroup discovery rules in a market problem},
	booktitle = {2nd International Conference on Machine Intelligence (ACIDCA-ICMI)},
	year = {2005},
	pages = {610-617},
	address = {Tozeur (Tunisia)},
	author = {F. J. Berlanga and M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera}
}
@inbook {inbook,
	title = {Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms},
	year = {2005},
	month = {01},
	pages = {85-96},
	doi = {10.1007/3-540-32400-3_7},
	author = {Lozano, Manuel and F. Herrera and J. R. Cano}
}
@inbook {Cano2005,
	title = {Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining},
	booktitle = {Evolutionary Computation in Data Mining},
	year = {2005},
	pages = {21{\textendash}39},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As instance selection can be viewed as a search problem, it could be solved using evolutionary algorithms.},
	isbn = {978-3-540-32358-7},
	doi = {10.1007/3-540-32358-9_2},
	url = {https://doi.org/10.1007/3-540-32358-9_2},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel},
	editor = {Ghosh, Ashish and Jain, Lakhmi C.}
}
@article {simidat24,
	title = {Stratification for Scaling Up Evolutionary Prototype Selection},
	journal = {Pattern Recognition Letters},
	volume = {26},
	year = {2005},
	pages = {953-963},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@inbook {691,
	title = {T{\'e}cnicas de reducci{\'o}n de datos en KDD},
	booktitle = {Miner{\'\i}a de datos: T{\'e}cnicas y Aplicaciones},
	number = {13-33},
	year = {2005},
	publisher = {Herrera F., Riguelme J.C. y Aguilar-Ruiz, JS},
	organization = {Herrera F., Riguelme J.C. y Aguilar-Ruiz, JS},
	address = {Sevilla (Espa{\~n}a)},
	issn = {84-921873-7-9},
	author = {J. R. Cano and F. Herrera}
}
@article {article,
	title = {A fast genetic method for inducting descriptive fuzzy models},
	journal = {Fuzzy Sets and Systems},
	volume = {141},
	year = {2004},
	month = {01},
	pages = {33-46},
	doi = {10.1016/S0165-0114(03)00112-X},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e}}
}
@conference {simidat12,
	title = {Algoritmo Evolutivo de Extracci{\'o}n de reglas de Asociaci{\'o}n aplicado a un problema de marketing},
	booktitle = {III Congreso Espa{\~n}ol de Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados(MAEB)},
	year = {2004},
	pages = {102-104},
	address = {C{\'o}rdoba (Spain)},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@conference {article,
	title = {Algoritmo GA-P Difuso para la Generaci{\'o}n de Controladores en Edificios Inteligentes},
	booktitle = {Congreso espa{\~n}ol de metaheur{\'\i}sticas, algoritmos evolutivos y bioinspirados.},
	year = {2004},
	month = {01},
	address = {C{\'o}rdoba (Espa{\~n}a)},
	author = {Lopez, Antonio and Doctor, Faiyaz and S{\'a}nchez, Luciano}
}
@inbook {696,
	title = {Extacci{\'o}n de conocimiento con algoritmos evolutivos y reglas difusas},
	booktitle = {Introducci{\'o}n a la miner{\'\i}a de datos},
	year = {2004},
	pages = {383-420},
	publisher = {Pearson Prentice Hall},
	organization = {Pearson Prentice Hall},
	issn = {84-205-4091-9},
	author = {del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and P. Gonz{\'a}lez and F. Herrera}
}
@conference {simidat13,
	title = {Extracci{\'o}n de reglas DNF difusas en un problema de marketing},
	booktitle = {XII Congreso Espa{\~n}ol de Tecnolog{\'\i}as y L{\'o}gica Difusa(ESTYLF)},
	year = {2004},
	pages = {351-356},
	address = {Ja{\'e}n (Spain)},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@article {simidat14,
	title = {Induction of Fuzzy Rule Based Classifiers with Evolutionary Boosting Algorithms},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {12},
	number = {3},
	year = {2004},
	pages = {296-308},
	author = {M. J. del Jesus and F. Hoffmann and L. Junco and L. S{\'a}nchez}
}
@conference {simidat10,
	title = {Predicci{\'o}n de series temporales mediante la coevoluci{\'o}n de funciones base},
	booktitle = {Tercer Congreso Espa{\~n}ol de Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados MAEB},
	year = {2004},
	month = {February},
	pages = {585-592},
	author = {A.J. Rivera-Rivas and I. Rojas and J. Ortega Lopera}
}
@conference {article,
	title = {Preliminary results on the application of boosting to learn weighted fuzzy rules under single-winner inference},
	booktitle = {Congreso espa{\~n}ol de metaheur{\'\i}sticas, algoritmos evolutivos y bioinspirados.},
	year = {2004},
	address = {C{\'o}rdoba-Espa{\~n}a},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e} and Su{\'a}rez, Mar{\'\i}a}
}
@inbook {inbook,
	title = {Proyecto KEEL: Desarrollo de una herramienta para el an{\'a}lisis e implementaci{\'o}n de algoritmos de extracci{\'o}n de conocimiento evolutivos},
	year = {2004},
	month = {01},
	pages = {413-424},
	isbn = {84-688-8442-1},
	author = {Alcala-Fdez, Jesus and M. J. del Jesus and Garrell, Josep-Maria and F. Herrera and Mart{\'\i}nez, Cesar and S{\'a}nchez, Luciano}
}
@inbook {simidat15,
	title = {Selecci{\'o}n Evolutiva Estratificada de Conjuntos de Entrenamiento para la Obtenci{\'o}n de Bases de Reglas con un Alto Equilibrio entre Precisi{\'o}n e Interpretabilidad},
	booktitle = {Tendencias de la Miner{\'\i}a de Datos en Spain.},
	year = {2004},
	pages = {263 - 274},
	isbn = {84-688-8442-1},
	author = {J. R. Cano and F. Herrera and M. Lozano},
	editor = {R. Gir{\'a}ldez and J. C. Riquelme and J. S. Aguilar}
}
@article {article,
	title = {Supply Estimation Using Coevolutionary Genetic Algorithms in the Spanish Electrical Market},
	journal = {Applied Intelligence},
	volume = {21},
	year = {2004},
	month = {07},
	pages = {7-24},
	doi = {10.1023/B:APIN.0000027764.76082.00},
	author = {Mar{\'\i}n, Enrique and S{\'a}nchez, Luciano}
}
@inbook {697,
	title = {Un estudio emp{\'\i}rico preliminar sobre los tests estad{\'\i}sticos m{\'a}s habituales en el aprendizaje autom{\'a}tico},
	booktitle = {Tendencias de la miner{\'\i}a de datos en Espa{\~n}a},
	number = {403-412},
	year = {2004},
	publisher = {R. Gir{\'a}ldez, J.C. Riquelme, J.S. Aguilar},
	organization = {R. Gir{\'a}ldez, J.C. Riquelme, J.S. Aguilar},
	address = {Santander (Espa{\~n}a)},
	issn = {84-688-8442-1},
	author = {F. Herrera and Hervas-Mart{\'\i}nez, Cesar and Otero, J and S{\'a}nchez, Luciano}
}
@conference {simidat9,
	title = {Una estrategia difusa para la aplicaci{\'o}n de operadores en un algoritmo evolutivo},
	booktitle = {XII Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy(ESTYLF)},
	year = {2004},
	month = {September},
	pages = {593-598},
	address = {Ja{\'e}n},
	author = {A.J. Rivera-Rivas and I. Rojas and J. Ortega Lopera and M. J. del Jesus}
}
@article {Otero2003,
	title = {3D motion estimation of bubbles of gas in fluid glass, using an optical flow gradient technique extended to a third dimension},
	journal = {Machine Vision and Applications},
	volume = {14},
	number = {3},
	year = {2003},
	month = {Jul},
	pages = {185{\textendash}191},
	abstract = {To solve the problem of estimating velocities of gas bubbles in melted glass, a method based on optical flow constraint (OFC) has been extended to the 3D case. A single camera, whose distance to the fluid is variable in time, is used to capture a sequence of frames at different depths. Since objects are not static, we cannot obtain two frames of different height values at the same time, and to our knowledge, this prevents the use of common 3D motion estimation techni ques. Since the information will be rather sparse, our estimation takes several measures around a given pixel and discards the erroneous ones, using a robust estimator. Along with the exposition of the practical application, the estimation proposed here is first contrasted in the 2D case to common benchmarks and then evaluated for a synthetic problem where velocities are known.},
	issn = {1432-1769},
	doi = {10.1007/s00138-002-0117-7},
	url = {https://doi.org/10.1007/s00138-002-0117-7},
	author = {Otero, J. and Otero, A. and S{\'a}nchez, L.}
}
@inbook {CORDON2003315,
	title = {- A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems},
	booktitle = {Intelligent Systems for Information Processing},
	year = {2003},
	pages = {315 - 326},
	publisher = {Elsevier Science},
	organization = {Elsevier Science},
	address = {Amsterdam},
	abstract = {Publisher Summary This chapter illustrates a multiobjective genetic algorithm for feature selection and database learning in Fuzzy Rule-Based Classification System (FRBCS). An FRBCS presents two main components{\textemdash}the Inference System and the Knowledge Base (KB). The KB is composed of the Rule Base (RB) constituted by the collection of fuzzy rules, and of the Data Base (DB), containing the membership functions of the fuzzy partitions associated to the linguistic variables. The composition of the KB of an FRBCS directly depends on the problem being solved. If there is no expert information about the problem under solving, an automatic learning process must be used to derive the KB from examples. This contribution proposes a multiobjective genetic process for jointly performing feature selection and DB components learning that is combined with an efficient fuzzy classification rule generation method to obtain the complete KB for a descriptive FRBCS. This method achieves an important reduction of the relevant variables selected for the final system and adapts the fuzzy partition of each variable to the problem being solved. Therefore, the conclusion is that the proposed method allows for enhancing interpretability, accuracy, and performance of the FRBCS method.},
	isbn = {978-0-444-51379-3},
	doi = {https://doi.org/10.1016/B978-044451379-3/50026-1},
	url = {http://www.sciencedirect.com/science/article/pii/B9780444513793500261},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and L. Magdalena and A.M. S{\'a}nchez and P. Villar},
	editor = {Bernadette Bouchon-Meunier and Laurent Foulloy and Ronald R. Yager}
}
@inbook {Cord{\'o}n2003,
	title = {A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems},
	booktitle = {Interpretability Issues in Fuzzy Modeling},
	year = {2003},
	pages = {79{\textendash}99},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy Rule-Based Classification System (FRBCS) and to automatically learn the whole Data Base definition using a non linear scaling function to adapt the fuzzy partition contexts and determining an appropiate granularity for each of them. An ad-hoc data covering learning method is considered to obtain the Rule Base. The method uses a multiobjective genetic algorithm in order to obtain a good trade-off between accuracy and interpretability.},
	isbn = {978-3-540-37057-4},
	doi = {10.1007/978-3-540-37057-4_4},
	url = {https://doi.org/10.1007/978-3-540-37057-4_4},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Magdalena, Luis and Villar, Pedro},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@conference {simidat7,
	title = {An Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining},
	booktitle = {Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications},
	year = {2003},
	month = {September},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {inproceedings,
	title = {Co-Evolutionary Algorithm for RBF by Self- Organizing Population of neurons},
	volume = {2686},
	year = {2003},
	month = {06},
	pages = {470-477},
	doi = {10.1007/3-540-44868-3_60},
	author = {Rivera Rivas, Antonio and Ortega, Julio and Rojas, Ignacio and M. J. del Jesus}
}
@conference {simidat8,
	title = {Extracci{\'o}n Evolutiva de Reglas de Asociaci{\'o}n en un Servicio de Urgencias Psiqui{\'a}tricas},
	booktitle = {II Congreso espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos evolutivos y bioinspirados(MAEB)},
	year = {2003},
	pages = {548-555},
	address = {Gij{\'o}n(Spain)},
	author = {J. Aguilera and M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Nav{\'\i}o and J. S{\'a}inz}
}
@inbook {702,
	title = {GA-P Based Search of Structures and Parameters of Dynamital Process Models},
	booktitle = {Advances in Soft Computing},
	year = {2003},
	pages = {371-380},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	issn = {1-85233-755-9},
	author = {Ben{\'\i}tez, Jos{\'e} Manuel and O. Cord{\'o}n and Hoffmann, Frank and Rajkumar, Roy}
}
@conference {inproceedings,
	title = {Genetic Search of Block-Based Structures of Dynamical Process Models},
	volume = {2686},
	year = {2003},
	month = {06},
	pages = {526-533},
	doi = {10.1007/3-540-44868-3_67},
	author = {Lopez, Antonio and S{\'a}nchez, Luciano}
}
@article {simidat5,
	title = {Linguistic Modeling with Hierarchical Systems of Weighted Linguistic Rules},
	journal = {International Journal of Approximate Reasoning},
	volume = {32},
	number = {2-3},
	year = {2003},
	pages = {187-215},
	author = {R. Alcal{\'a} and J. R. Cano and O. Cord{\'o}n and F. Herrera and P. Villar and I. Zwir}
}
@conference {simidat2,
	title = {Optimizaci{\'o}n de redes de RBFs mediante cooperaci{\'o}n-competici{\'o}n de neuronas y algoritmos de minimizaci{\'o}n de error},
	booktitle = {MAEB},
	year = {2003},
	month = {February},
	pages = {499-508},
	author = {A.J. Rivera-Rivas and I. Rojas and J. Ortega Lopera and M. J. del Jesus}
}
@conference {inproceedings,
	title = {Optimizing Supply Strategies in the Spanish Electrical Market.},
	volume = {2687},
	year = {2003},
	month = {06},
	pages = {353-360},
	doi = {10.1007/3-540-44869-1_45},
	author = {Mar{\'\i}n, Enrique and S{\'a}nchez, Luciano}
}
@conference {simidat6,
	title = {Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms},
	booktitle = {Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications},
	year = {2003},
	month = {September},
	author = {M. Lozano and F. Herrera and J. R. Cano}
}
@inbook {S{\'a}nchez2003,
	title = {Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?},
	booktitle = {Accuracy Improvements in Linguistic Fuzzy Modeling},
	year = {2003},
	pages = {366{\textendash}385},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {The accuracy of linguistic classifiers can be improved with several techniques, but they all compromise the interpretability of the rule base up to a certain degree. Assigning weights to fuzzy rules and tuning the memberships associated to linguistic variables are two of the most common methods. In this work we study whether tuning the membership functions in a linguistic classifier is better or not than adjusting rule weights, in terms of the interpretability of the rule base and the complexity of the output.},
	isbn = {978-3-540-37058-1},
	doi = {10.1007/978-3-540-37058-1_15},
	url = {https://doi.org/10.1007/978-3-540-37058-1_15},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e}},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@article {simidat4,
	title = {Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: an Experimental Study},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {7},
	number = {6},
	year = {2003},
	pages = {561-575},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {CanoCHS02,
	title = {A GRASP Algorithm for Clustering},
	booktitle = {Proceedings of the 8th Ibero-American Conference on Artifical Intelligence, Seville, Spain, November 12-15, 2002,},
	year = {2002},
	pages = {214{\textendash}223},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@article {CanoCHS02,
	title = {A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure},
	journal = {Journal of Intelligent and Fuzzy Systems},
	volume = {12},
	number = {3-4},
	year = {2002},
	pages = {235{\textendash}242},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@conference {737,
	title = {Aprendizaje jer{\'a}rquico por refuerzo mediante programaci{\'o}n gen{\'e}tica dirigida por gram{\'a}ticas},
	booktitle = {Congreso Espa{\~n}ol de algoritmos evolutivos y bioinspirados AEB-02},
	year = {2002},
	month = {01},
	address = {M{\'e}rida (Espa{\~n}a)},
	author = {Carbajal, Santiago and S{\'a}nchez, Luciano}
}
@conference {743,
	title = {Aproximaciones de Evoluci{\'o}n con Funciones Difusas Mediante Cooperacion y Competici{\'o}n de RBFs},
	booktitle = {Congreso Espa{\~n}ol de Algoritmos Evolutivos y Bioinspirados AEB-02},
	year = {2002},
	month = {01},
	address = {M{\'e}rida, (Espa{\~n}a)},
	author = {A.J. Rivera-Rivas and Ortega, Julio and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and Gonz{\'a}lez-Pe{\~n}alvez, Jes{\'u}s}
}
@conference {740,
	title = {Modelos Evolutivos de Extracci{\'o}n de Conocimiento en Aplicaciones M{\'e}dicas: Enfermedad de Parkinson y Urgencias PSiqui{\'a}tricas},
	booktitle = {Workshop de Miner{\'\i}a de Datos y Aprendizaje Autom{\'a}tico},
	year = {2002},
	month = {01},
	address = {Santander (Espa{\~n}a)},
	author = {Jos{\'e} Aguilera and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and P. Gonz{\'a}lez and F. Herrera and Nav{\'\i}o, M. and Saiz, J.}
}
@conference {738,
	title = {Optimizaci{\'o}n gen{\'e}tico borrosa de par{\'a}metros en un sistema de bajo coste para la medici{\'o}n visual de cotas de veh{\'\i}culos},
	booktitle = {Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy},
	year = {2002},
	month = {01},
	address = {Le{\'o}n (Espa{\~n}a)},
	author = {Otero, Jos{\'e} and Otero, Adolfo and Junco, Luis and S{\'a}nchez, Luciano}
}
@conference {742,
	title = {Scrae Web: Sistema de Correcci{\'o}n y Revisi{\'o}n Autom{\'a}tica de Ex{\'a}menes a Trav{\'e}s de la Web},
	booktitle = {Jornadas de Ense{\~n}anza Universitaria de la Inform{\'a}tica Jenui },
	year = {2002},
	month = {01},
	address = {C{\'a}ceres (Espa{\~n}a)},
	author = {Sanchez Pulido, Alfredo and J. R. Cano and Pav{\'o}n-Pulido, Nieves}
}
@conference {739,
	title = {Selecci{\'o}n Evolutiva de Instancias en Miner{\'\i}a de Datos},
	booktitle = {Workwhop de Miner{\'\i}a de Datos y Aprendizaje Autom{\'a}tico},
	year = {2002},
	month = {01},
	address = {Santander (Espa{\~n}a)},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel}
}
@article {SANCHEZ2002175,
	title = {Some relationships between fuzzy and random set-based classifiers and models},
	journal = {International Journal of Approximate Reasoning},
	volume = {29},
	number = {2},
	year = {2002},
	pages = {175 - 213},
	abstract = {When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is doing its best, because the precision gap between the best understandable model and the best black-box model is not known. In this paper we discuss how to generate probabilistic rule-based models and classifiers with the same structure as fuzzy rule-based ones. Fuzzy models, in which features are partitioned into linguistic terms, will be compared to probabilistic rule-based models with the same number of terms in every linguistic partition. We propose to use these probabilistic models to estimate a lower precision limit which fuzzy rule learning algorithms should surpass.},
	keywords = {Fuzzy classifiers, fuzzy models, Random set-based classifiers, Random set-based models},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/S0888-613X(01)00063-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X01000639},
	author = {Luciano S{\'a}nchez and Jorge Casillas and O. Cord{\'o}n and M. J. del Jesus}
}
@conference {741,
	title = {Utilizaci{\'o}n de Algoritmos Gen{\'e}ticos Multiobjetivos para la Selecci{\'o}n de Caracter{\'\i}sticas y Dise{\~n}o de la Base de Conocimiento de un Sistema de Clasificaci{\'o}n Basado en Reglas Difusas},
	booktitle = {Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy},
	year = {2002},
	month = {01},
	address = {Le{\'o}n (Espa{\~n}a)},
	author = {O. Cord{\'o}n and F. Herrera and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and Magdalena, Luis and A.M. S{\'a}nchez and Villar, Pedro}
}
@conference {943781,
	title = {A fast genetic method for inducting linguistically understandable fuzzy models},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1559-1563 vol.3},
	abstract = {Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods.},
	keywords = {Artificial intelligence, backfitting, boosting techniques, computational linguistics, extended additive models, fast genetic method, fuzzy logic, fuzzy models, Fuzzy reasoning, fuzzy rule bases, fuzzy rule learning methods, fuzzy set theory, Fuzzy sets, genetic algorithm, genetic algorithms, inference mechanisms, learning (artificial intelligence), Learning systems, linguistically understandable fuzzy model induction, mixtures of experts, reasoning methods, residuals, submodel fitting, Training data, uncertainty handling},
	doi = {10.1109/NAFIPS.2001.943781},
	author = {L. S{\'a}nchez}
}
@conference {inproceedings,
	title = {A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems},
	volume = {3},
	year = {2001},
	month = {08},
	pages = {1253 - 1258 vol.3},
	isbn = {0-7803-7078-3},
	doi = {10.1109/NAFIPS.2001.943727},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and Villar, P}
}
@article {SANCHEZ2001175,
	title = {Combining GP operators with SA search to evolve fuzzy rule based classifiers},
	journal = {Information Sciences},
	volume = {136},
	number = {1},
	year = {2001},
	note = {Recent Advances in Genetic Fuzzy Systems},
	pages = {175 - 191},
	abstract = {The genotype{\textendash}phenotype encoding of fuzzy rule bases in GA, along with their corresponding crossover and mutation operators, can be used by other search schemes, improving the behavior of these last ones. As a practical consequence of this, a simulated annealing-based method for inducting both parameters and structure of a fuzzy classifier has been developed. The adjacency operator in SA has been replaced with a macromutation taken from tree-shaped genotype GAs. We will show that results of SA search are similar to those of GP in both the efficiency of the learned classifiers and in its linguistic interpretability, while the memory consumption of the learning process is lower.},
	keywords = {Fuzzy classification, genetic algorithms, Genetic programming, Simulated annealing},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/S0020-0255(01)00146-3},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025501001463},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and J.A. Corrales}
}
@article {article,
	title = {Design of RBF Networks by Cooperative/Competitive Evolution of Units},
	year = {2001},
	month = {04},
	doi = {10.1007/978-3-7091-6230-9_93},
	author = {Rivera Rivas, Antonio and Ortega, Julio and Prieto, A}
}
@conference {10.1007/3-540-45497-7_29,
	title = {Feature Selection Algorithms Applied to Parkinson{\textquoteright}s Disease},
	booktitle = {Medical Data Analysis},
	year = {2001},
	pages = {195{\textendash}200},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In Parkinson{\textquoteright}s Disease an analysis of Medical Data could highlight some symptoms, which can be used as a complementary tool in an early diagnosis. This paper analyses some Filter and Wrapper Feature Selection Algorithms and combinations of them that determine some relevant features in relation to this problem. The experimentation carried out with a data set of patients allows us to determine a set of different premorbid personality traits that can be considered in the early diagnosis of Parkinsonism.},
	isbn = {978-3-540-45497-7},
	author = {Nav{\'\i}o, M. and Jos{\'e} Aguilera and M. J. del Jesus and Gonz{\'a}lez, R. and F. Herrera and Ir{\'\i}bar, C.},
	editor = {Crespo, Jose and Maojo, Victor and Martin, Fernando}
}
@article {simidat161,
	title = {Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process},
	journal = {Information Sciences},
	volume = {136},
	year = {2001},
	pages = {135-157},
	author = {J. Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {CASILLAS2001135,
	title = {Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems},
	journal = {Information Sciences},
	volume = {136},
	number = {1},
	year = {2001},
	note = {Recent Advances in Genetic Fuzzy Systems},
	pages = {135 - 157},
	abstract = {The inductive learning of a fuzzy rule-based classification system (FRBCS) is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high-classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods.},
	keywords = {feature selection, Fuzzy reasoning methods, fuzzy rule-based classification systems, Inductive learning},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/S0020-0255(01)00147-5},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025501001475},
	author = {J Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {943783,
	title = {Genetic tuning of fuzzy rule-based systems integrating linguistic hedges},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1570-1574 vol.3},
	abstract = {Tuning fuzzy rule-based systems for linguistic modeling is an interesting and widely developed task. It involves adjusting the membership functions composing the knowledge base. To do that, changing the parameters defining each membership function as using linguistic hedges to slightly modify them may be considered. This paper introduces a genetic tuning process for jointly making these two tuning approaches. The experimental results show that our method obtains accurate linguistic models in both approximation and generalization aspects.},
	keywords = {Computer science, experimental results, fuzzy logic, Fuzzy rule-based systems, Fuzzy sets, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, genetic tuning process, knowledge base, knowledge based systems, linguistic hedges, linguistic modeling, membership functions, Proposals, Shape, Takagi-Sugeno model, Timing, uncertainty handling},
	doi = {10.1109/NAFIPS.2001.943783},
	author = {J. Casillas and O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
@conference {inproceedings,
	title = {Graph Based GP Applied to Dynamical Systems Modeling},
	volume = {2084},
	year = {2001},
	month = {06},
	pages = {725-732},
	doi = {10.1007/3-540-45720-8_87},
	author = {Lopez, Antonio and L{\'o}pez Garc{\'\i}a, Hilario and S{\'a}nchez, Luciano}
}
@conference {733,
	title = {JavaScript como lenguaje de apoyo a la docencia v{\'\i}a web},
	booktitle = {Jornadas de la ense{\~n}anza universitaria Jenui},
	year = {2001},
	month = {07},
	address = {Palma de Mallorca (Espa{\~n}a)},
	author = {Rivas, Victor M. and Jos{\'e} R. Balsas and Jos{\'e} Aguilera and Ure{\~n}a Lopez, L.Alfonso}
}
@conference {inproceedings,
	title = {Optimizing RBF Networks with Cooperative/Competitive Evolution of Units and Fuzzy Rules},
	year = {2001},
	month = {06},
	pages = {570-578},
	doi = {10.1007/3-540-45720-8_68},
	author = {Rivera Rivas, Antonio and Ortega, Julio and Rojas, Ignacio and Prieto, Alberto}
}
@conference {735,
	title = {Selecting Fuzzy-Ruled Based Classification System with Specific Reasoning Methods Using Genetical Algorithms},
	booktitle = {Joint 9th IFSA World congres and 20th Nafips International},
	year = {2001},
	month = {07},
	address = {Praga},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@conference {726,
	title = {Acceso restringido a calificaciones de alumnos a trav{\'e}s de internet},
	booktitle = {Simposio Espa{\~n}ol de inform{\'a}tica distribuida},
	year = {2000},
	month = {09},
	address = {Orense (Espa{\~n}a)},
	author = {Jos{\'e} Aguilera and Jos{\'e} R. Balsas and Francisco Mart{\'\i}nez and Rivas, Victor M.}
}
@conference {724,
	title = {Adaptaci{\'o}n curricular de la asignatura-Teor{\'\i}a de algortimos},
	booktitle = {Jornadas sobre la ense{\~n}anza universitaria de la inform{\'a}tica},
	year = {2000},
	month = {09},
	address = {Alcal{\'a} de Henares (Espa{\~n}a)},
	author = {Jos{\'e} Aguilera and Jos{\'e} R. Balsas}
}
@conference {727,
	title = {Aportaciones de la ingenier{\'\i}a de la programaci{\'o}n al curriculum del ingeniero t{\'e}cnico industrial},
	booktitle = {Jornadas sobre la ense{\~n}anza universitaria de la inform{\'a}tica},
	year = {2000},
	month = {09},
	address = {Alcal{\'a} de Henares (Espa{\~n}a)},
	author = {Jos{\'e} R. Balsas and Jos{\'e} Aguilera}
}
@conference {717,
	title = {Approximating The Discrete Space Equation From Chaotic Noisy Data},
	booktitle = {International Conference On Information Processing and Management Of Uncertainty In Knowledge-Based System},
	year = {2000},
	month = {01},
	url = {http://di002.edv.uniovi.es/~luciano/documentos/c25.pdf},
	author = {Fern{\'a}ndez, A. and S{\'a}nchez, Luciano and Navarro, J.J.}
}
@inbook {Cord{\'o}n2000,
	title = {Different Proposals to Improve the Accuracy of Fuzzy Linguistic Modeling},
	booktitle = {Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications},
	year = {2000},
	pages = {189{\textendash}221},
	publisher = {Springer US},
	organization = {Springer US},
	address = {Boston, MA},
	abstract = {Nowadays, Linguistic Modeling is considered as one of the most important applications of Fuzzy Set Theory, along with Fuzzy Control. Linguistic models have the advantage of providing a human-readable description of the system modeled in the form of a set of linguistic rules. In this contribution, we will analyze several approaches to improve the accuracy of linguistic models while maintaining their descriptive power. All these approaches will share the common idea of improving the way in which the Fuzzy Rule-Based System performs interpolative reasoning by improving the cooperation between the rules in the linguistic model Knowledge Base.},
	isbn = {978-1-4615-4513-2},
	doi = {10.1007/978-1-4615-4513-2_9},
	url = {https://doi.org/10.1007/978-1-4615-4513-2_9},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and Villar, Pedro and Zwir, Igor},
	editor = {Ruan, Da and Kerre, Etienne E.}
}
@conference {720,
	title = {Evoluci{\'o}n de la asignatura metodolog{\'\i}a y tecnolog{\'\i}a de la programaci{\'o}n},
	booktitle = {Jenui},
	year = {2000},
	month = {01},
	address = {Alcal{\'a} de Henares (Espa{\~n}a)},
	author = {Jos{\'e} Aguilera and Jos{\'e} R. Balsas and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e}}
}
@inbook {inbook,
	title = {GA-P based Feature Selection and Classification combining different Methods of Texture Analysis},
	year = {2000},
	month = {09},
	pages = {209-219},
	isbn = {978-981-02-4364-7},
	doi = {10.1142/9789812792563_0017},
	author = {Cano, Fernando and Junco, Luis and S{\'a}nchez, Luciano}
}
@conference {728,
	title = {Influencia del uso de modificadores ling{\"u}{\'\i}sticos y grados de certeza en un sistema de clasificaci{\'o}n basados en reglas difusas},
	booktitle = {X Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y reglas difusas},
	year = {2000},
	month = {09},
	address = {Santander (Espa{\~n}a)},
	author = {M. J. del Jesus and F. Herrera}
}
@article {843495,
	title = {Interval-valued GA-P algorithms},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {4},
	number = {1},
	year = {2000},
	month = {April},
	pages = {64-72},
	abstract = {When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on interval GA-P can devise an interval model from examples and provide the algebraic expression that best approximates the data. The method is useful for generating a confidence interval for the output of a model, and also for obtaining a robust point estimate from data which we know to contain outliers. The algorithm was applied to a real problem related to electrical energy distribution. Classical methods were applied first, and then the interval GA-P. The results of both studies are used to compare interval GA-P with GP, GA-P, classical regression methods, neural networks, and fuzzy models.},
	keywords = {Arithmetic, Computer science, confidence interval, electrical energy distribution, Fuzzy neural networks, Fuzzy systems, genetic algorithms, Genetic programming, Neural networks, point estimate, Robustness, statistical analysis, symbol manipulation, symbolic regression},
	issn = {1089-778X},
	doi = {10.1109/4235.843495},
	author = {L. S{\'a}nchez}
}
@conference {719,
	title = {La asignatura de planificaci{\'o}n de sistemas inform{\'a}ticos en ingenier{\'\i}a t{\'e}cnica  en inform{\'a}tica de gesti{\'o}n.},
	booktitle = {Jenui},
	year = {2000},
	month = {01},
	address = {Alcala de Henares (Espa{\~n}a)},
	author = {P. Gonz{\'a}lez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@conference {722,
	title = {Modelo gr{\'a}fico de transporte de agua a trav{\'e}s del estrecho de Gibraltar a partir de im{\'a}genes de satelites},
	booktitle = {Jornadas de an{\'a}lisis de variables y simulaci{\'o}n num{\'e}rica del intercambio de masas de agua a trav{\'e}s del estrecho de Gibraltar},
	year = {2000},
	month = {06},
	address = {C{\'a}diz (Espa{\~n}a)},
	author = {Molina-Aguilar, Andr{\'e}s and Jos{\'e} Aguilera}
}
@article {article,
	title = {Niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction},
	year = {2000},
	month = {01},
	author = {S{\'a}nchez, Luciano and Antonio Corrales Gonz{\'a}lez, Jos{\'e}}
}
@conference {725,
	title = {Objetos distribuidos versus paso de mensajes como soporte para la representaci{\'o}n de abstracciones distribuidas},
	booktitle = {Simposio Espa{\~n}ol de inform{\'a}tica distribuida},
	year = {2000},
	month = {09},
	address = {Orense (Espa{\~n}a)},
	author = {Mart{\'\i}nez del Rio, Francisco and Jos{\'e} R. Balsas and Rivas, Victor M. and Capel-Tu{\~n}on, Manuel Isidoro}
}
@conference {731,
	title = {Un sistema coordinador de recursos distribuidos},
	booktitle = {Jornadas cient{\'\i}ficas en tecnolog{\'\i}as de la informaci{\'o}n},
	year = {2000},
	month = {11},
	address = {C{\'a}diz (Espa{\~n}a)},
	author = {L{\'o}pez, Juan Antonio and Jos{\'e} R. Balsas and A.J. Rivera-Rivas}
}
@conference {723,
	title = {Un sistema interactivo autom{\'a}tico de aprendizaje y evaluaci{\'o}n},
	booktitle = {Congreso de innovaci{\'o}n educativa en ense{\~n}anzas t{\'e}cnicas},
	year = {2000},
	month = {09},
	address = {Donostia- San Sebasti{\'a}n (Espa{\~n}a)},
	author = {Jos{\'e} R. Balsas and Jos{\'e} Aguilera and Garc{\'\i}a Vega, Manuel and Rivas-Santos, Victor M.}
}
@conference {730,
	title = {Una propuesta para la formaci{\'o}n en las tecnolog{\'\i}as Web},
	booktitle = {Nuevas tecnolog{\'\i}as aplicadas a la educaci{\'o}n},
	year = {2000},
	month = {11},
	author = {A.J. Rivera-Rivas and Jos{\'e} R. Balsas}
}
@conference {729,
	title = {Using the adabooth algorithm to induce fuzzy rules in classification problems},
	booktitle = {X Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Difusa},
	year = {2000},
	month = {09},
	address = {Santander (Espa{\~n}a)},
	author = {Junco, Luis and S{\'a}nchez, Luciano}
}
@article {simidat162,
	title = {A proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems},
	journal = {International Journal of Approximate Reasoning},
	volume = {20},
	year = {1999},
	pages = {21-45},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {simidat164,
	title = {Analyzing the Reasoning Mechanisms in Fuzzy Rule-Based Classification Systems},
	journal = {Mathware \& Soft Computing},
	volume = {5},
	year = {1999},
	pages = {321-332},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {Cordn1999EvolutionaryAT,
	title = {Evolutionary approaches to the learning of fuzzy rule-based classification systems},
	year = {1999},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
@conference {inproceedings,
	title = {Evolving Fuzzy Rule Based Classifiers with GA-P: A Grammatical Approach},
	volume = {1598},
	year = {1999},
	month = {05},
	pages = {203-210},
	doi = {10.1007/3-540-48885-5_17},
	author = {Carbajal, Santiago and Gonz{\'a}lez Martinez, Ferm{\'\i}n and S{\'a}nchez, Luciano}
}
@conference {716,
	title = {Hibridaci{\'o}n de m{\'e}todos filtro y de envoltura para selecci{\'o}n de caracter{\'\i}sticas},
	booktitle = {Conferencia de la asociaci{\'o}n Espa{\~n}ola para la inteligencia artificial. III Jornada de transferencia tecnol{\'o}gica de inteligencia artificial.},
	year = {1999},
	month = {01},
	address = {Murcia (Espa{\~n}a)},
	author = {Aguilera Garc{\'\i}a, Jose Joaquin and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@article {simidat163,
	title = {MOGUL: A Methodology to Obtain Genetic fuzzy rule-based systems Ander the iterative rule Learning Approach},
	journal = {International Journal of Intelligent Systems},
	volume = {14},
	year = {1999},
	pages = {1123-1153},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {714,
	title = {Programaci{\'o}n de multicomputadoras basadas en objetos distribuidos},
	booktitle = {SEID99: Simposio espa{\~n}ol de inform{\'a}tica distribuida},
	year = {1999},
	month = {02},
	address = {Santiago de Compostela},
	author = {Mart{\'\i}nez del Rio, Francisco and Sanchez Pedro and Capel-Tu{\~n}on, Manuel Isidoro and Jos{\'e} R. Balsas}
}
@conference {715,
	title = {Simulaci{\'o}n de fluidos din{\'a}micos mediante aut{\'o}matas celulares.},
	booktitle = {Congreso Espa{\~n}ol de inform{\'a}tica gr{\'a}fica.},
	year = {1999},
	month = {06},
	address = {Ja{\'e}n},
	author = {M.D. P{\'e}rez-Godoy and Andr{\'e}s Molina and Sanchez Pedro}
}
@article {Cord{\'o}n1999,
	title = {Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques},
	journal = {Applied Intelligence},
	volume = {10},
	number = {1},
	year = {1999},
	month = {Jan},
	pages = {5{\textendash}24},
	abstract = {Real-world electrical engineering problems can take advantage of the last Data Analysis methodologies. In this paper we will show that Genetic Fuzzy Rule-Based Systems and Genetic Programming techniques are good choices for tackling with some practical modeling problems. We claim that both evolutionary processes may produce good numerical results while providing us with a model that can be interpreted by a human being. We will analyze in detail the characteristics of these two methods and we will compare them to the some of the most popular classical statistical modeling methods and neural networks.},
	issn = {1573-7497},
	doi = {10.1023/A:1008384630089},
	url = {https://doi.org/10.1023/A:1008384630089},
	author = {O. Cord{\'o}n and F. Herrera and S{\'a}nchez, Luciano}
}
@article {SANCHEZ1998343,
	title = {A random sets-based method for identifying fuzzy models},
	journal = {Fuzzy Sets and Systems},
	volume = {98},
	number = {3},
	year = {1998},
	pages = {343 - 354},
	abstract = {The nature of interference sources in signal processing is a key problem in many applied disciplines. These interferences are often modelled by random processes, although it has been shown that many models can be favourably modified when some of the uncertainty sources are treated as fuzzy experiments. Following this spirit, the objective of this paper is to build a mathematical model which explains a set of imprecise measurements taken on a physical system. Furthermore, it is assumed that the effect of unmodelled inputs to the system can be regarded as a random process, but the imprecision in the measures is better described by means of a possibility distribution.},
	keywords = {Control theory, Description length, Linguistic modelling, Random set},
	issn = {0165-0114},
	doi = {https://doi.org/10.1016/S0165-0114(97)00010-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0165011497000109},
	author = {Luciano S{\'a}nchez}
}
@article {758,
	title = {Analyzing the Reasoning Mechanism in Fuzzy Rule-Based Classification Systems},
	journal = {Mathware \& Soft Computing},
	volume = {5},
	year = {1998},
	pages = {321-332},
	issn = {1134-5632},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/3-540-64582-9_778,
	title = {Computing the spanish medium electrical line maintenance costs by means of evolution-based learning processes},
	booktitle = {Methodology and Tools in Knowledge-Based Systems},
	year = {1998},
	pages = {478{\textendash}486},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we deal with the problem of computing the maintenance costs of electrical medium line in spanish towns. To do so, we present two Data Analysis tools taking as a base Evolutionary Algorithms, the Interval Genetic Algorithm-Programming method to perform symbolic regression and Genetic Fuzzy Rule-Based Systems to design fuzzy models, and use them to solve the said problem. Results obtained are compared with other kind of techniques: classical regression and neural modeling.},
	isbn = {978-3-540-69348-2},
	author = {O. Cord{\'o}n and F. Herrera and S{\'a}nchez, Luciano},
	editor = {Mira, Jos{\'e} and del Pobil, Angel Pasqual and Ali, Moonis}
}
@conference {711,
	title = {Estimaci{\'o}n de la Longitud de L{\'\i}nea de Baja Tensi{\'o}n Mediante T{\'e}cnicas Evolutivas de An{\'a}lisis de Datos},
	booktitle = {8{\textordfeminine} Reuni{\'o}n Nacional de Grupos de Investigaci{\'o}n en Ingenier{\'\i}a El{\'e}ctrica},
	year = {1998},
	author = {O. Cord{\'o}n and Sp{\'\i}n, Antonio and Fajardo, Waldo and F. Herrera and S{\'a}nchez, Luciano}
}
@article {simidat165,
	title = {Genetic Learning of Fuzzy Rule-Based Classification Systems Cooperating with Fuzzy Reasoning Methods},
	journal = {International Journal of Intelligent Systems},
	volume = {13},
	number = {10/11},
	year = {1998},
	pages = {1025-1053},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Learning from imprecise examples with GA-P algorithms},
	journal = {Mathware \& soft computing, ISSN 1134-5632, Vol. 5, N{\textordmasculine}. 2-3, 1998, pags. 305-319},
	volume = {5},
	year = {1998},
	month = {01},
	author = {S{\'a}nchez, Luciano and Couso, In{\'e}s}
}
@article {article,
	title = {Localization and fuzzy classification of manufacturing defects in sheets of glass},
	volume = {5},
	year = {1998},
	month = {01},
	author = {Junco, Luis and S{\'a}nchez, Luciano}
}
@conference {713,
	title = {M{\'e}todos de razonamiento aproximado basados en el concepto de mayor{\'\i}a difusa para sistemas de clasificaci{\'o}n},
	booktitle = {Congreso espa{\~n}ol sobre tecnolog{\'\i}a y l{\'o}gica fuzzy},
	year = {1998},
	month = {09},
	address = {Pamplona (Espa{\~n}a)},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@article {simidat166,
	title = {Modelado cualitativo utilizando una metodolog{\'\i}a evolutiva de aprendizaje iterativo de bases de reglas difusas},
	journal = {Revista Iberoamericana de Inteligencia Artificial},
	volume = {50},
	year = {1998},
	pages = {56-61},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and M. Lozano}
}
@conference {712,
	title = {Presentaci{\'o}n de TCL-TK para el desarrollo de aplicaciones por parte de usuarios no expertos en programaci{\'o}n},
	booktitle = {Jornadas cient{\'\i}ficas andaluzas en tecnolog{\'\i}a de la informaci{\'o}n},
	year = {1998},
	address = {C{\'a}diz (Espa{\~n}a)},
	author = {P. Gonz{\'a}lez and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy}
}
@conference {inproceedings,
	title = {An evolutionary paradigm for designing fuzzy rule-based systems from examples},
	year = {1997},
	month = {10},
	pages = {139 - 144},
	isbn = {0-85296-693-8},
	doi = {10.1049/cp:19971170},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Lozano, Manuel}
}
@conference {710,
	title = {Herramientas para el desarrollo de pr{\'a}cticas en una asignatura de redes de computadores de una ingenier{\'\i}a t{\'e}cnica},
	booktitle = {Jornadas de inform{\'a}tica},
	year = {1997},
	address = {C{\'a}diz (Espa{\~n}a)},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy}
}
@conference {708,
	title = {Nuevos m{\'e}todos de razonamiento en sistemas de clasificaci{\'o}n basados en reglas difusas},
	booktitle = {Congreso espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy.},
	year = {1997},
	address = {Tarragona},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@conference {707,
	title = {Sistema de Clasificaci{\'o}n con Reglas Difusas Utilizando Algoritmos Gen{\'e}ticos},
	booktitle = {VI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF{\textquoteright}96)},
	year = {1996},
	address = {Oviedo},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@conference {10.1007/3-540-60406-5_7,
	title = {Using a symbolic model checker for verify safety properties in SA/RT models},
	booktitle = {Software Engineering {\textendash}- ESEC {\textquoteright}95},
	year = {1995},
	pages = {59{\textendash}75},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, structured methods are integrated with formal verification methods based on temporal logic. The goal is to use an operational method (SA/RT) for the system behavioural specification, and to complement this with safety properties expressed in a declarative style using temporal logic (CTL). These properties are checked against the operational specification using a model checker (SMV).},
	isbn = {978-3-540-45552-3},
	author = {Tuya, Javier and S{\'a}nchez, Luciano and Corrales, Jose A.},
	editor = {Sch{\"a}fer, Wilhelm and Botella, Pere}
}
@conference {10.1007/3-540-57209-0_21,
	title = {A pragmatic task design approach based on a Ward/Mellor real-time structured specification},
	booktitle = {Software Engineering {\textendash}- ESEC {\textquoteright}93},
	year = {1993},
	pages = {301{\textendash}312},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {This paper shows some details about the development of a distributed system for the control and supervision of all processes related to the coal feeding a thermal power station. Starting from a structured specification based on the Ward/Mellor methodology for real-time systems, a method for systematic design and implementation is built. The method can be automated in the future, and includes a series of guidelines, rules, procedures, reusable components and graphical representations to successfully derive the implementation from the structured specification.},
	isbn = {978-3-540-47972-7},
	author = {Tuya, J. and S{\'a}nchez, L. and Zurita, R. and Corrales, J. A.},
	editor = {Sommerville, Ian and Paul, Manfred}
}
@conference {10.1007/3-540-56798-4_201,
	title = {Combination of self-organizing maps and multilayer perceptrons for speaker independent isolated word recognition},
	booktitle = {New Trends in Neural Computation},
	year = {1993},
	pages = {550{\textendash}555},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {A new Neural Network architecture that combines the Kohonen Self-Organizing Maps and Multilayer Perceptrons for a speech recognition task is presented. This architecture overcomes the problem of time-alignement of the succesive frames obtained from one utterance of one word: the succesive frames of a word generate a trajectory in a two-dimensional space using the Self-Organizing Map. These are classified using the Perceptron. Comparation with other techniques are made, and results are better than the obtained wich Trace Segmentation. The vocabulary used in the experiments is a highly difficult subset from the Spanish alphabet: the Spanish E-Set.},
	isbn = {978-3-540-47741-9},
	author = {Tuya, J. and Arias, E. and S{\'a}nchez, L. and Corrales, J. A.},
	editor = {Mira, Jos{\'e} and Cabestany, Joan and Prieto, Alberto}
}
