@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 {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 {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 {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}
}
@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}
}
@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 {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 {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}
}
@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}
}
@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}
}
@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 {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 {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}
}
@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 {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 {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 {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}
}
@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}
}
@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}
}
@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 {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}
}
@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 {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}
}
@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}
}
@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 {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 {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 {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}
}
@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 {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}
}
@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}
}
@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 {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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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 {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}
}
@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}
}
@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 {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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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}
}
@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 {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 {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}
}
@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 {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}
}
@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}
}
@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}
}
@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 {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}
}
@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 {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 = {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}
}
@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}
}
@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 {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 {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}
}
@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 {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}
}
@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}
}
@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 {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 {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}
}
@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}
}
@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 {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}
}
@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}
}
@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}
}
@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}
}
@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 {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 {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 {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 {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}
}
@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}
}
@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}
}
@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}
}
@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 {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}
}
@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 {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 {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 {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}
}
@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}
}
@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}
}
@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 {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 {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 {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 {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}
}
@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}
}
@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 {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 {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}
}
@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 {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}
}
@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}
}
@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}
}
@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 {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}
}
@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}
}
@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 {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}
}
@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}
}
@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 {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 {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}
}
@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}
}
@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}
}
@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 {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 {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}
}
