@article {812,
	title = {Strategies for time series forecasting with generalized regression neural networks},
	journal = {Neurocomputing},
	volume = {491},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {509-521},
	doi = {https://doi.org/10.1016/j.neucom.2021.12.028},
	author = {Francisco Mart{\'\i}nez and Francisco Charte and Mar{\'\i}a Pilar Fr{\'\i}as and Ana Mar{\'\i}a Mart{\'\i}nez-Rodr{\'\i}guez}
}
@article {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 {799,
	title = {E2PAMEA: un algoritmo evolutivo para la extracci{\'o}ni eficiente de patrones emergentes difusos en entornos big data},
	booktitle = {Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence},
	year = {2021},
	author = {A.M. Garc{\'\i}a-Vico and D. Elizondo and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona}
}
@article {Charte:ClassInformedAEs,
	title = {Reducing Data Complexity using Autoencoders with Class-informed Loss Functions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {In Press},
	year = {2021},
	note = {PID2019-107793GB-I00},
	publisher = {IEEE},
	abstract = {Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separability or boundary shapes, as well as group morphology. Many techniques can transform the data in order to find better features, but few focus on specifically reducing data complexity. Most data transformation methods mainly treat the dimensionality aspect, leaving aside the available information within class labels which can be useful when classes are somehow complex. This paper proposes an autoencoder-based approach to complexity reduction, using class labels in order to inform the loss function about the adequacy of the generated variables. This leads to three different new feature learners, Scorer, Skaler and Slicer. They are based on Fisher{\textquoteright}s discriminant ratio, the Kullback-Leibler divergence and least-squares support vector machines, respectively. They can be applied as a preprocessing stage for a binary classification problem. A thorough experimentation across a collection of 27 datasets and a range of complexity and classification metrics shows that class-informed autoencoders perform better than 4 other popular unsupervised feature extraction techniques, especially when the final objective is using the data for a classification task.},
	issn = {0162-8828},
	doi = {10.1109/TPAMI.2021.3127698},
	author = {David Charte and Francisco Charte and Herrera, Francisco}
}
@conference {Charte:HAIS21,
	title = {Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2021},
	note = {PID2020-119478GB-I00},
	pages = {305{\textendash}315},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Datasets from real-world applications usually deal with many variables and present difficulties when modeling them with traditional classifiers. There is a variety of feature selection and extraction tools that may help with the dimensionality problem, but most of them do not focus on the complexity of the classes. In this paper, a new autoencoder-based model for addressing class complexity in data is introduced, aiming to extract features that present classes in a more separable fashion, thus simplifying the classification task. This is possible thanks to a combination of the standard reconstruction error with a least-squares support vector machine loss function. This model is then applied to a practical use case: classification of chest X-rays according to the presence of COVID-19, showing that learning features that increase linear class separability can boost classification performance. For this purpose, a specific convolutional autoencoder architecture has been designed and trained using the recently published COVIDGR dataset. The proposed model is evaluated by means of several traditional classifiers and metrics, in order to establish the improvements caused by the extracted features. The advantages of using a feature learner and traditional classifiers are also discussed.},
	isbn = {978-3-030-86271-8},
	author = {David Charte and Sevillano-Garc{\'\i}a, Iv{\'a}n and Lucena-Gonz{\'a}lez, Mar{\'\i}a Jes{\'u}s and Mart{\'\i}n-Rodr{\'\i}guez, Jos{\'e} Luis and Francisco Charte and Herrera, Francisco},
	editor = {Sanjurjo Gonz{\'a}lez, Hugo and Pastor L{\'o}pez, Iker and Garc{\'\i}a Bringas, Pablo and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@article {782,
	title = {A Comprehensive and Didactic Review on Multilabel Learning Software Tools},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {TIN2015-68854-R},
	month = {03/2020},
	pages = {50330-50354},
	abstract = {Machine learning has become an everyday tool in so many fields that there is plenty of software to run many of these algorithms in every device, from supercomputers to embedded appliances. Most of these methods fall into the category known as standard learning, being supervised models (guided by pre-labeled examples) aimed to classify new patterns into exactly one category. This way, machine learning is in charge of getting rid of junk emails, labeling people in a picture, or detecting a fraudulent transaction when using a credit card. Aside from unsupervised learning methods, which are usually applied to group similar patterns, infer association rules and similar tasks, some non-standard supervised machine learning problems have been faced in late years. Among them, multilabel learning is arguably the most popular one. These algorithms aim to produce models in which each data pattern may be linked to several categories at once. Thus, a multilabel classifier generates a set of outputs instead of only one as a standard classifier does. However, software tools for multilabel learning tend to be scarce. This paper provides multilabel researchers with a comprehensive review of the currently available multilabel learning software. It is written following a didactic approach, focusing on how to accomplish each task rather than simply offering a list of programs and websites. The goal is to help finding the most appropriate resource to complete every step, from locating datasets and partitioning them to running many of the multilabel algorithms proposed in the literature until now.},
	keywords = {machine learning, multilabel, non-standard learning, software, Tools},
	doi = {https://doi.org/10.1109/ACCESS.2020.2979787},
	author = {Francisco Charte}
}
@article {783,
	title = {An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges},
	journal = {Neurocomputing},
	volume = {404},
	year = {2020},
	note = {TIN2015-68854-R; TIN2017-89517-P; DeepSCOP Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica en Big Data 2018},
	pages = {93-107},
	abstract = {In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.},
	keywords = {Autoencoders, Deep learning, Feature extraction, Representation learning},
	doi = {https://doi.org/10.1016/j.neucom.2020.04.057},
	author = {David Charte and Francisco Charte and M. J. del Jesus and F. Herrera}
}
@article {784,
	title = {Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications},
	journal = {Neurocomputing},
	volume = {410},
	year = {2020},
	note = {TIN2017-85827-P; RTI2018-098913-B-I00; PSI2015-65848-R; PGC2018-098813-B-C31; PGC2018-098813-B-C32; RTI2018-101114-B-I; TIN2017-90135-R; RTI2018-098743-B-I00; RTI2018-094645-B-I00; FPU15/06512; FPU17/04154; FJCI-2017{\textendash}33022; UMA18-FEDERJA-084; ED431C2017/12; ED431G/08; ED431C2018/29; Y2018/EMT-5062; ED431F2018/02; U01 AG024904; W81XWH-12-2-0012},
	pages = {237-270},
	abstract = {Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.},
	keywords = {AI for social well-being, Alzheimer, Artificial intelligence (AI), Artificial neural networks (ANNs), Autism, Big Data, Computational neuroethology, Deep learning, Dyslexia, Emotion recognition, evolutionary computation, Glaucoma, Human{\textendash}machine interaction, machine learning, Neuroscience, Ontologies, Parkinson, Reinforcement learning, Robotics, Virtual reality},
	doi = {https://doi.org/10.1016/j.neucom.2020.05.078},
	author = {Juan M.G{\'o}rriz and Javier Ram{\'\i}rez and Andr{\'e}s Ort{\'\i}z and Francisco J. Mart{\'\i}nez-Murcia and Ferm{\'\i}n Segovia and John Suckling and Matthew Leming and Yu-Dong Zhang and Jos{\'e} Ram{\'o}n {\'A}lvarez-S{\'a}nchez and Guido Bologna and Paula Bonomini and Fernando E. Casado and David Charte and Francisco Charte and Ricardo Contreras and Alfredo Cuesta Infante and Richard J. Duro and Antonio Fern{\'a}ndez Caballero and Eduardo Fern{\'a}ndez Jover and Pedro G{\'o}mez Vilda and Manuel Gra{\~n}a and F. Herrera and Roberto Iglesias and Anna Lekova and Javier de Lope and Ezequiel L{\'o}pez Rubio and Rafael Mart{\'\i}nez Tom{\'a}s and Miguel A. Molina-Cabello and Antonio S. Montemayor and Paulo Novais and Daniel Palacios-Alonso and Juan J. Pantrigo and Bryson R. Payne and F{\'e}lix de la Paz L{\'o}pez and Mar{\'\i}a Ang{\'e}lica Pinninghoff and Mariano Rinc{\'o}n and Jos{\'e} Santos and Karl Thurnhofer-Hemsi and Athanasios Tsanas and Ramiro Varela and Jose M. Ferr{\'a}ndez}
}
@article {774,
	title = {Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines},
	journal = {Information Fusion},
	volume = {54},
	year = {2020},
	note = {TIN2015-68454-R},
	month = {02/2020},
	pages = {44-60},
	abstract = {Classifying data patterns is one of the most recurrent applications in machine learning. The number of input features influences the predictive performance of many classification models. Most classifiers work with high-dimensional spaces. Therefore, there is a great interest in facing the task of reducing the input space. Manifold learning has been shown to perform better than classical dimensionality reduction approaches, such as Principal Component Analysis and Linear Discriminant Analysis. In this sense, Autoencoders (AEs) provide an automated way of performing feature fusion, finding the best manifold to reconstruct the data. There are several models and architectures of AEs. For this reason, in this study an exhaustive analysis of the predictive performance of different AEs models with a large number of datasets is proposed, aiming to provide a set of useful guidelines. These will allow users to choose the appropriate AE model for each case, depending on data traits and the classifier to be used. A thorough empirical analysis is conducted including four AE models, four classification paradigms and a group of datasets with a variety of traits. A convenient set of rules to follow is obtained as a result.},
	keywords = {Autoencoders, classification, Deep learning, Dimensionality reduction, Feature fusion},
	doi = {https://doi.org/10.1016/j.inffus.2019.07.004},
	author = {Pulgar, Francisco J. and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@article {765,
	title = {E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments},
	journal = {Neurocomputing},
	volume = {415},
	year = {2020},
	note = {TIN2015-68454-R; BES-2016-07773},
	month = {11/2020},
	pages = {60-73},
	abstract = {In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also employs a token-competition-based procedure for updating an elite population where the best set of patterns found so far is stored. In addition, a novel MapReduce procedure for an efficient computation of the evaluation function employed for guiding the search process is proposed. The method, called Bit-LUT employs a pre-evaluation stage where data is represented as a look-up table made of bit sets. This look-up table can be employed later in the chromosome evaluation by means of bitwise operations, reducing the computational complexity of the process.

The experimental study carried out shows that E2PAMEA is a promising alternative for the extraction of high-quality emerging patterns in big data. In addition, the proposed Bit-LUT evaluation shows a significant improvement on efficiency with a great scalability capacity on both dimensions of data, which enables the processing of massive datasets faster than other alternatives.},
	keywords = {Emerging pattern mining Evolutionary fuzzy systems Multi-objective evolutionary algorithm Big data},
	doi = {https://doi.org/10.1016/j.neucom.2020.07.007},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and D. Elizondo and C. J. Carmona}
}
@article {779,
	title = {El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodolog{\'\i}as y herramientas},
	journal = {Ense{\~n}anza y Aprendizaje de Ingenier{\'\i}a de Computadores},
	number = {10},
	year = {2020},
	abstract = {La transici{\'o}n de una modalidad de ense{\~n}anza tradicional y presencial a una de tipo remoto, provocada por el confinamiento a ra{\'\i}z del coronavirus SARS-CoV-2, ha implicado cambios que han debido realizarse de manera acelerada y que afectan no solo al desarrollo de las clases, sino tambi{\'e}n a las actividades pr{\'a}cticas en laboratorio, de comunicaci{\'o}n y de evaluaci{\'o}n de las competencias de los estudiantes. En este trabajo exponemos c{\'o}mo hemos afrontado dicha transici{\'o}n en asignaturas del {\'a}rea Arquitectura y tecnolog{\'\i}a de computadores en la Universidad de Ja{\'e}n, as{\'\i} como la planificaci{\'o}n que hemos llevado a cabo para el pr{\'o}ximo curso 2020/2021 ante el nivel de incertidumbre sobre c{\'o}mo se desarrollar{\'a}.},
	keywords = {Aprendizaje activo, Aprendizaje basado en proyectos, Metodolog{\'\i}as de aprendizaje, Modelos de evaluaci{\'o}n},
	doi = {http://dx.doi.org/10.30827/Digibug.64779},
	author = {Francisco Charte and A.J. Rivera-Rivas and Medina, J. and Espinilla, Macarena}
}
@article {773,
	title = {EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search},
	journal = {Integrated Computer-Aided Engineering},
	volume = {27},
	number = {3},
	year = {2020},
	month = {05/2020},
	pages = {211-231},
	abstract = {Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time. },
	keywords = {Autoencoder, evolutionary methods, Representation learning},
	doi = {https://doi.org/10.3233/ICA-200619},
	author = {Francisco Charte and A.J. Rivera-Rivas and Francisco Mart{\'\i}nez and M. J. del Jesus}
}
@conference {772,
	title = {A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy},
	booktitle = {International Work-Conference on Artificial Neural Networks},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {270-280},
	abstract = {A convenient data preprocessing has proven to be crucial in order to achieve high levels of accuracy, time series being no exception. For this kind of forecasting tasks, several specialized preprocessing methods have been described, being trend analysis and seasonal analysis some of them. Several have been formally grouped around a methodology that is always applied to state of the art time series forecasting models, like the well known ARIMA.

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

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

This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, ruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.},
	keywords = {Autoencoders, Deep learning, Feature learning},
	doi = {https://doi.org/10.1007/978-3-030-19651-6_40},
	author = {David Charte and Francisco Charte and M. J. del Jesus and F. Herrera}
}
@article {297,
	title = {A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations},
	journal = {Progress in Artificial Intelligence},
	volume = {8},
	year = {2019},
	note = {TIN2017-89517-P; TIN2015-68454-R},
	month = {11},
	pages = {1-14},
	abstract = {Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.},
	issn = {2192-6360},
	doi = {10.1007/s13748-018-00167-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and F. Herrera}
}
@conference {776,
	title = {Automatic Time Series Forecasting with GRNN: A Comparison with Other Models},
	booktitle = {International Work-Conference on Artificial Neural Networks},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {198-209},
	abstract = {In this paper a methodology based on general regression neural networks for forecasting time series in an automatic way is presented. The methodology is aimed at achieving an efficient and fast tool so that a large amount of time series can be automatically predicted. In this sense, general regression neural networks present some interesting features, they have a fast single-pass learning and produce deterministic results. The methodology has been implemented in the R environment. A study of packages in R for automatic time series forecasting, including well-known statistical and computational intelligence models such as exponential smoothing, ARIMA or multilayer perceptron, is also done, together with an experimentation on running time and forecast accuracy based on data from the NN3 forecasting competition.},
	keywords = {Automatic forecasting, General regression neural networks, time series forecasting},
	doi = {https://doi.org/10.1007/978-3-030-20521-8_17},
	author = {Francisco Mart{\'\i}nez and Francisco Charte and A.J. Rivera-Rivas and Fr{\'\i}as, Mar{\'\i}a Pilar}
}
@conference {771,
	title = {Automating Autoencoder Architecture Configuration: An Evolutionary Approach},
	booktitle = {International Work-Conference on the Interplay Between Natural and Artificial Computation},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {339-349},
	abstract = {Learning from existing data allows building models able to classify patterns, infer association rules, predict future values in time series and much more. Choosing the right features is a vital step of the learning process, specially while dealing with high-dimensional spaces. Autoencoders (AEs) have shown ability to conduct manifold learning, compressing the original feature space without losing useful information. However, there is no optimal AE architecture for all datasets. In this paper we show how to use evolutionary approaches to automate AE architecture configuration. First, a coding to embed the AE configuration in a chromosome is proposed. Then, two evolutionary alternatives are compared against exhaustive search. The results show the great superiority of the evolutionary way.},
	keywords = {Autoencoder, Deep learning, Evolutionary, Optimization},
	doi = {https://doi.org/10.1007/978-3-030-19591-5_35},
	author = {Francisco Charte and A.J. Rivera-Rivas and Francisco Mart{\'\i}nez and M. J. del Jesus}
}
@article {292,
	title = {Dealing with difficult minority labels in imbalanced mutilabel data sets},
	journal = {Neurocomputing},
	volume = {326},
	year = {2019},
	note = {TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765},
	pages = {39{\textendash}53},
	abstract = {Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In addition, this characteristic challenges many of the existent preprocessing algorithms. Furthermore, the concurrence between imbalanced labels can make harder the learning from certain labels. These are what we call difficult labels. In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed. Specific metrics to assess this trait in multilabel datasets, called SCUMBLE (Score of ConcUrrence among iMBalanced LabEls) and SCUMBLELbl, are presented along with REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels), a new algorithm aimed to relax label concurrence. How to deal with this problem using the R mldr package is also outlined.},
	doi = {10.1016/j.neucom.2016.08.158},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@book {304,
	title = {El pasado de la computaci{\'o}n personal. Historia de la microinform{\'a}tica (2a Edici{\'o}n)},
	year = {2019},
	publisher = {Editorial Universidad de Ja{\'e}n},
	organization = {Editorial Universidad de Ja{\'e}n},
	abstract = {By the end of the 2010s computers have become ubiquitous. Users are not even aware that they carry them: in their cellphones, smart watches, and other similar devices. This evolution has been made possible as a result of the revolution that took place in the 1970s and the 1980s. It was then, with the emergence of microprocessors and microcomputers, that computing began to arrive at people{\textquoteright}s houses. This book recovers the history of those devices.},
	isbn = {978-84-9159-186-3},
	author = {Francisco Charte and Lina Garc{\'\i}a}
}
@article {777,
	title = {predtoolsTS: R package for streamlining time series forecasting},
	journal = {Progress in Artificial Intelligence},
	volume = {8},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {06/2019},
	pages = {505{\textendash}510},
	abstract = {Time series forecasting is a field of interest in many areas. Classically, statistical methods have been used to address this problem. In recent years, machine learning (ML) algorithms have been also applied with satisfactory results. However, ML software packages are not skilled to deal with raw sequences of temporal data, and therefore, it is necessary to transform these time series. This paper presents predtoolsTS, an R package that provides a uniform interface for applying both statistical and ML methods to time series forecasting. predtoolsTS comprises four modules: preprocessing, modeling, prediction and postprocessing, in order to deal with the whole process of time series forecasting.},
	keywords = {machine learning, R, time series forecasting},
	doi = {https://doi.org/10.1007/s13748-019-00193-z},
	author = {Francisco Charte and Alberto Vico and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@article {293,
	title = {REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization},
	journal = {Neurocomputing},
	volume = {326},
	year = {2019},
	note = {TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765},
	pages = {110{\textendash}122},
	abstract = {The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification. A handful of multilabel resampling methods have been proposed in late years, aiming to balance the labels distribution. However, these methods have to face a new obstacle, specific for multilabel data, as is the joint appearance of minority and majority labels in the same data patterns. We presented recently a new algorithm designed to decouple imbalanced labels concurring in the same instance, called REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels). The goal of this work is to propose REMEDIAL-HwR (REMEDIAL Hybridization with Resampling), a procedure to hybridize this method with some of the best resampling algorithms available in the literature, including random oversampling, heuristic undersampling and synthetic sample generation techniques. These hybrid methods are then empirically analyzed, determining how their behavior is influenced by the label decoupling process. The analysis of results shows that the proposed method improves certain classifiers performance when it is applied over imbalanced datasets with label concurrence. In addition, a noteworthy set of guidelines on the combined use of these techniques can be drawn from the conducted experimentation.},
	doi = {10.1016/j.neucom.2017.01.118},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {295,
	title = {Ruta: implementations of neural autoencoders in R},
	journal = {Knowledge-Based Systems},
	volume = {174},
	year = {2019},
	note = {TIN2015-68854-R,BigDaP-TOOLS},
	month = {06/2019},
	pages = {4-8},
	abstract = {Autoencoders are neural networks which perform feature learning on data. Many variants can be found in the literature, but their implementations are scarce, in separate software pieces and utilizing different languages and frameworks. The ruta package implements a unified foundation for the construction and training of autoencoders on top of Keras and Tensorflow, and allows for easy access to the main functionalities as well as full customization of their diverse aspects.},
	doi = {-},
	author = {David Charte and F. Herrera and Francisco Charte}
}
@article {796,
	title = {Smartdata: Data preprocessing to achieve smart data in R},
	journal = {Neurocomputing},
	volume = {360},
	year = {2019},
	note = {BigDaP-TOOLS - Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica 2016},
	month = {09/2019},
	pages = {1-13},
	abstract = {As the amount of data available exponentially grows, data scientists are aware that finding the value in the data is key to a successful data exploiting. However, the data rarely presents itself in a ordered, clean way. In opposition to dealing with raw data, the term smart data is becoming more and more visible both in the specialized literature and companies. While software packages publicly exist to deal with raw data, there is no unified framework that encompasses all the required fields to transform such raw data to smart data. In this paper, the novel smartdata package is introduced. Written in R and available at CRAN repository, it includes the most recent and relevant algorithms to treat raw data from multiple perspectives, now unified under a simple yet powerful API, which enables the data scientist to easily pipeline their application. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript.},
	keywords = {Data preprocessing, machine learning, Preprocessing, Smart data},
	doi = {https://doi.org/10.1016/j.neucom.2019.06.006},
	author = {I. Cordon and Luengo, Juli{\'a}n and Garc{\'\i}a, Salvador and F. Herrera and Francisco Charte}
}
@article {778,
	title = {Time Series Forecasting with KNN in R: the tsfknn Package},
	journal = {The R Journal},
	volume = {11},
	number = {2},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {12/2019},
	pages = {229-242},
	abstract = {In this paper the tsfknn package for time series forecasting using k-nearest neighbor regression is described. This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies and among different functions to aggregate the targets of the nearest neighbors. It is also possible to assess the forecast accuracy of the KNN model.},
	doi = {https://doi.org/10.32614/RJ-2019-004},
	author = {Francisco Mart{\'\i}nez and Fr{\'\i}as, Mar{\'\i}a Pilar and Francisco Charte and A.J. Rivera-Rivas}
}
@conference {307,
	title = {A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders},
	booktitle = {19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018},
	year = {2018},
	note = {TIN2015-68854-R; FPU16/00324},
	month = {11},
	pages = {439{\textendash}447},
	address = {Madrid (Spain)},
	abstract = {The problem of high dimensionality is a challenge when facing machine learning tasks. A high dimensional space has a negative effect on the predictive performance of many methods, specifically, classification algorithms. There are different proposals that arise to mitigate the effects of this phenomenon. In this sense, models based on deep learning have emerged.},
	isbn = {978-3-030-03493-1},
	doi = {10.1007/978-3-030-03493-1_46},
	author = {F. Pulgar-Rubio and Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus}
}
@conference {333,
	title = {A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {-},
	month = {10},
	pages = {949{\textendash}950},
	address = {Granada (Spain)},
	abstract = {This is a summary of our article published in Information Fusion to be part of the CAEPIA-18 KeyWorks.},
	isbn = {978-88-61970-00-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@article {Charte2018,
	title = {A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations},
	journal = {Progress in Artificial Intelligence},
	year = {2018},
	month = {Nov},
	abstract = {Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.},
	issn = {2192-6360},
	doi = {10.1007/s13748-018-00167-7},
	url = {https://doi.org/10.1007/s13748-018-00167-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and F. Herrera}
}
@article {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}
}
@conference {331,
	title = {An{\'a}lisis del impacto de datos desbalanceados en el rendimiento predictivo de redes neuronales convolucionales},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {TIN2015-68454-R; FPU16/00324},
	month = {10},
	pages = {1213{\textendash}1218},
	address = {Granada (Spain)},
	abstract = {En los {\'u}ltimos a{\~n}os han surgido nuevas propuestas basadas en Deep Learning para afrontar la tarea de clasificaci{\'o}n. Estas propuestas han obtenido buenos resultados en algunos campos, por ejemplo, en reconocimiento de im{\'a}genes. Sin embargo, existen factores que deben ser analizados para valorar su influencia en los resultados obtenidos con estos nuevos modelos. En este trabajo se analiza la clasificaci{\'o}n de datos desbalanceados con redes neuronales convolucionales (Convolutional Neural Networks-CNNs). Para hacerlo, se han llevado a cabo una serie de tests donde se reconocen im{\'a}genes mediante CNNs. As{\'\i}mismo, se utilizan conjuntos de datos con diferente grado de desbalanceo. Los resultados demuestran que el desequilibrio afecta negativamente al rendimiento predictivo.},
	isbn = {978-88-61970-00-7},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and Francisco Charte and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@article {298,
	title = {Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {8},
	year = {2018},
	note = {-},
	pages = {67{\textendash}83},
	abstract = {The design and manufacture of hardware is expensive, both in time and in economic investment, which is why integrated circuits are always manufactured in large volume, to take advantage of economies of scale. For this reason, the majority of processors manufactured are general purpose, thus expanding its range of applications. In recent years, however, more and more processors are being manufactured for specific applications, including those aimed at accelerating work with deep neural networks. This article introduces the need for this type of specialized hardware, describing its purpose, operation and current implementations.},
	issn = {2173-8688},
	author = {A.J. Rivera-Rivas and Francisco Charte and Espinilla, Macarena and M.D. P{\'e}rez-Godoy}
}
@article {291,
	title = {Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository},
	journal = {Neurocomputing},
	volume = {289},
	year = {2018},
	note = {TIN2014-57251-P,TIN2015-68454-R,BigDaP-TOOLS},
	pages = {68{\textendash}85},
	abstract = {New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years. The experimentation associated with each of them always goes through the same phases: selection of datasets, partitioning, training, analysis of results and, finally, comparison with existing methods. This last step is often hampered since it involves using exactly the same datasets, partitioned in the same way and using the same validation strategy. In this paper we present a set of tools whose objective is to facilitate the management of multi-label datasets, aiming to standardize the experimentation procedure. The two main tools are an R package, mldr.datasets, and a web repository with datasets, Cometa. Together, these tools will simplify the collection of datasets, their partitioning, documentation and export to multiple formats, among other functions. Some tips, recommendations and guidelines for a good experimental analysis of multi-label methods are also presented.},
	doi = {10.1016/j.neucom.2018.02.011},
	author = {Francisco Charte and A.J. Rivera-Rivas and David Charte and M. J. del Jesus and F. Herrera}
}
@conference {332,
	title = {Una primera aproximaci{\'o}n a la predicci{\'o}n de variables tur{\'\i}sticas con Deep Learning},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {TIN2015-68454-R},
	month = {10},
	pages = {939{\textendash}943},
	address = {Granada (Spain)},
	abstract = {El turismo es una de las actividades econ{\'o}micas m{\'a}s importantes a nivel mundial, por lo que una correcta planificaci{\'o}n de los recursos existentes en funci{\'o}n de la demanda es fundamental. En este sentido, el trabajo desarrollado permite comparar la bondad de un nuevo modelo de deep learning, LSTM, frente a un modelo cl{\'a}sico ampliamente reconocido, ARIMA. Se ha llevado a cabo un proceso de entrenamiento para obtener los modelos LSTM y ARIMA que, posteriormente se han validado utilizando datos no disponibles durante el aprendizaje. Nuestros resultados muestran que los nuevos modelos LSTM obtienen una precisi{\'o}n mayor que el cl{\'a}sico ARIMA, tanto en la validaci{\'o}n a priori como en la predicci{\'o}n posterior.},
	isbn = {978-88-61970-00-7},
	author = {Daniel Trujillo Viedma and A.J. Rivera-Rivas and Francisco Charte and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@conference {312,
	title = {A first approach towards a fuzzy decision tree for multilabel classification},
	booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2017},
	note = {TIN2014- 57251-P,P11-TIC-7765},
	month = {7},
	pages = {1{\textendash}6},
	address = {Naples (Italy)},
	abstract = {This paper proposes a multilabel fuzzy decision tree classifier named FuzzDTML. The algorithm uses generalized fuzzy entropy, aggregated over all labels, to choose the best attribute for growing the tree. The proposed algorithm also can generate leaves predicting partial label sets, which can incorporate to some degree the dependence among labels, as well as produce more interpretable models. An empirical analysis shows that, although the algorithm does not yet incorporate pruning nor fuzzy interval adjustment phases, it is competitive with other tree based approaches for multilabel classification, with better performance in data sets having numerical features that can be fuzzified.},
	isbn = {978-1-5090-6034-4},
	doi = {10.1109/FUZZ-IEEE.2017.8015521},
	author = {Prati, Ronaldo C and Francisco Charte and F. Herrera}
}
@conference {308,
	title = {A specialized lazy learner for time series forecasting},
	booktitle = {17th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2017},
	year = {2017},
	note = {TIN2015-68854-R},
	month = {7},
	pages = {1397{\textendash}1403},
	address = {Costa Ballena, Rota, C{\'a}adiz (Spain)},
	abstract = {In a time series context the nearest neighbour algorithm looks for the historical observations most similar to the latest observations of the time series. However, some nearest neighbours can be misleading. In this paper we propose that, if prior information about the structure of the time series is known, the search space of possible neighbours can be narrowed so that some possibly misleading neighbours are avoided. This way a more effective forecasting method can be obtained.},
	isbn = {978-84-617-8694-7},
	author = {Francisco Mart{\'\i}nez and M.P. Fr{\'\i}as and Francisco Charte and A.J. Rivera-Rivas}
}
@conference {309,
	title = {A Transformation Approach Towards Big Data Multilabel Decision Trees},
	booktitle = {14th International Work-Conference on Artificial Neural Networks (IWANN 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {73{\textendash}84},
	address = {C{\'a}diz (Spain)},
	abstract = {A large amount of the data processed nowadays is multilabel in nature. This means that every pattern usually belongs to several categories at once. Multilabel data are abundant, and most multilabel datasets are quite large. This causes that many multilabel classification methods struggle with their processing. Tackling this task by means of big data methods seems a logical choice. However, this approach has been scarcely explored by now. The present work introduces several big data multilabel classifiers, all of them based on decision trees. After detailing how they have been designed, their predictive performance, as well as the execution time, are analyzed.},
	isbn = {978-3-319-59152-0},
	doi = {10.1007/978-3-319-59153-7_7},
	author = {A.J. Rivera-Rivas and Francisco Charte and F. Pulgar-Rubio and M. J. del Jesus}
}
@article {289,
	title = {Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass},
	journal = {Computers \& Chemical Engineering},
	volume = {101},
	year = {2017},
	note = {ENE2014-60090-C2-2-R,TIN2015-68454-R},
	pages = {23{\textendash}30},
	abstract = {The production of biofuels is a process that requires the adjustment of multiple parameters. Performing experiments in which these parameters are changed and the outputs are analyzed is imperative, but the cost of these tests limits their number. For this reason, it is important to design models that can predict the different outputs with changing inputs, reducing the number of actual experiments to be completed. Response Surface Methodology (RSM) is one of the most common methods for this task, but machine learning algorithms represent an interesting alternative. In the present study the predictive performance of multiple models built from the same problem data are compared: the production of bioethanol from lignocellulosic materials. Four machine learning algorithms, including two neural networks, a support vector machine and a fuzzy system, together with the RSM method, are analyzed. Results show that Reg-CO2RBFN, the method designed by the authors, improves the results of all other alternatives.},
	doi = {10.1016/j.compchemeng.2017.02.008},
	author = {Francisco Charte and Inmaculada Romero and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Eulogio Castro}
}
@article {299,
	title = {Evoluci{\'o}n tecnol{\'o}gica del hardware de v{\'\i}deo y las GPU en los ordenadores personales},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {7},
	year = {2017},
	note = {-},
	pages = {111{\textendash}128},
	abstract = {This article provides a review of the most important milestones in the evolution of graphics hardware. Communication between computers and people has been advancing over time, reaching interactivity with the emergence of timesharing systems in the early 1960s. Personal computers, whose expansion began almost two decades later, used the visualization of information on a screen as the main means of communication with the user from the very beginning. The hardware in charge of this task has gradually evolved to become an indispensable part of the computer architecture, to such an extent that a large part of laptops and desktop computers incorporate the graphic hardware into the same integrated circuit that houses the microprocessor.},
	issn = {2173-8688},
	author = {Francisco Charte and Rueda, Antonio J. and Espinilla, Macarena and A.J. Rivera-Rivas}
}
@article {290,
	title = {Is the average photon energy a unique characteristic of the spectral distribution of global irradiance?},
	journal = {Solar Energy},
	volume = {149},
	year = {2017},
	note = {ENE2008-05098-ALT},
	pages = {32{\textendash}43},
	abstract = {The average photon energy (APE) has become a popular index to qualitatively assess whether shorter or longer wavelengths are enhanced in a specific spectral distribution of irradiance when compared to the AM1.5G standard spectrum. According to some previous assessments, this index might uniquely distinguish individual global tilted irradiance and global horizontal irradiance spectra. This paper basically applies the same methodology as that used in these studies, i.e., a statistical analysis based on spectral distributions grouped in 0.02-eV APE bins and their standard deviation across all 50-nm bands into which the wavelength range under scrutiny (350 to 1050 nm) is divided. Two years of spectral global tilted irradiance datasets collected at two Spanish locations, 333 km apart, are analyzed here.},
	doi = {10.1016/j.solener.2017.03.086},
	author = {Nofuentes, G and Gueymard, CA and J. Aguilera and M.D. P{\'e}rez-Godoy and Francisco Charte}
}
@conference {310,
	title = {Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO2RBFN},
	booktitle = {14th International Work-Conference on Artificial Neural Networks (IWANN 2017)},
	year = {2017},
	note = {TIN2015-68454-R},
	month = {6},
	pages = {733{\textendash}744},
	address = {C{\'a}diz (Spain)},
	abstract = {Research in renewable energies is a global trend. One remarkable area is the biomass transformation into biotehanol, a fuel that can replace fossil fuels. A key step in this process is the pretreatment stage, where several variables are involved. The experimentation for determining the optimal values of these variables is expensive, therefore it is necessary to model this process. This paper focus on modeling the production of biotehanol from olive tree biomass by data mining methods. Notably, the authors present Reg-CO2RBFN, an adaptation of a cooperative-competitive designing method for radial basis function networks. One of the main drawbacks in this modeling is the low number of instances in the data sets. To compare the results obtained by Reg-CO2RBFN, other well-known data mining regression methods are used to model the transformation process.},
	isbn = {978-3-319-59152-0},
	doi = {10.1007/978-3-319-59153-7_63},
	author = {Francisco Charte and Inmaculada Romero and A.J. Rivera-Rivas and Eulogio Castro}
}
@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 {300,
	title = {Uso de dispositivos FPGA como apoyo a la ense{\~n}anza de asignaturas de arquitectura de computadores},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {7},
	year = {2017},
	note = {-},
	pages = {37{\textendash}52},
	abstract = {Computer Engineering students in Spanish universities have to take one or more courses devoted to theur learning of computer architecture. The theoretical part of these subjects are usually focused on describing the architecture itself, while practical sessions are used to introduce assembly programming by means of a certain instruction set which runs into a software emulator. This paper proposes to supplement practical sessions, so that students learn to design a microprocessor by themselves from its basic components, by introducing the use of FPGA devices.},
	issn = {2173-8688},
	author = {Francisco Charte and Espinilla, Macarena and A.J. Rivera-Rivas and F. Pulgar-Rubio}
}
@conference {329,
	title = {An{\'a}lisis visual de t{\'e}cnicas no supervisadas de deep learning con el paquete dlvisR},
	booktitle = {XVII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2016)},
	year = {2016},
	note = {-},
	month = {9},
	pages = {895{\textendash}904},
	address = {Salamanca (Spain)},
	abstract = {Las t{\'e}cnicas de deep learning aplicadas al aprendizaje no supervisado han demostrado su utilidad y potencial, pero carecen del nivel de interpretabilidad que pueden proporcionar otros algoritmos. Adem{\'a}s, el ajuste de los par{\'a}metros de funcionamiento de este tipo de m{\'e}todos suele realizarse de forma autom{\'a}tica, y no se obtienen explicaciones de c{\'o}mo influyen en el comportamiento de los modelos y los resultados que estos ofrecen. En este trabajo se presenta una herramienta desarrollada para la plataforma R, el paquete dlvisR. Este proporciona un conjunto de utilidades para la visualizaci{\'o}n de las variables obtenidas internamente por este tipo de modelos respecto de par{\'a}metros ajustables por el usuario. Adem{\'a}s, un estudio sobre algunos conjuntos de datos reafirma la hip{\'o}tesis de que la modificaci{\'o}n de dichos par{\'a}metros tiene consecuencias observables visualmente, cuyo an{\'a}lisis podr{\'\i}a aportar conocimiento de inter{\'e}s.},
	isbn = {978-84-9012-632-5},
	author = {David Charte and Francisco Charte and F. Herrera}
}
@conference {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}
}
@article {301,
	title = {Explotaci{\'o}n de la potencia de procesamiento mediante paralelismo: un recorrido hist{\'o}rico hasta la GPGPU},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {6},
	year = {2016},
	note = {-},
	pages = {19{\textendash}33},
	abstract = {Due to the improvement of semiconductor manufacturing technologies, and higher integration scales in the last decades, the power of computing devices has experienced an impressive growth. However, some obstacles have been also found along the way. As a consequence, the battle for reaching higher clock frequencies almost ended a few years ago. Nowadays, the power of processors is not measured exclusively using GHz. Other factors, as the number of cores and their inner design, also have a large impact. This paper provides an historical review on how parallelism techniques have been adapted over time to overcome these changes aiming to better exploit the available hardware.},
	issn = {2173-8688},
	author = {Francisco Charte and A.J. Rivera-Rivas and F. Pulgar-Rubio and Mar{\'\i}a J del Jesu D{\'\i}az}
}
@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 {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 {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 {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 {328,
	title = {mldr: Paquete R para Exploraci{\'o}n de Datos Multietiqueta},
	booktitle = {XVI Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2015)},
	year = {2015},
	note = {TIN2012-33856,P10-TIC-06858,P11-TIC-9704},
	month = {11},
	pages = {695{\textendash}704},
	address = {Albacete (Spain)},
	abstract = {La clasificaci{\'o}n tradicional de datos trabaja con varios atributos de entrada y uno solo de salida. La clasificaci{\'o}n multietiqueta, por el contrario, implica predecir simult{\'a}neamente m{\'a}s de un atributo de salida, por lo que los algoritmos de clasificaci{\'o}n han de afrontar una tarea considerablemente m{\'a}s compleja. La clasificaci{\'o}n de datos multietiqueta es una labor de creciente importancia, presente en {\'a}mbitos como la categorizaci{\'o}n de textos, el etiquetado de im{\'a}genes o la identificaci{\'o}n de sonidos en un medio. mldr es un paquete R que incorpora una serie de herramientas para cargar y crear datasets multietiqueta, calcular diversas medidas de caracterizaci{\'o}n de los datos, generar varios gr{\'a}ficos que muestran su distribuci{\'o}n, y manipular los datasets para tratar de facilitar la tarea de los algoritmos de preprocesamiento y de clasificaci{\'o}n. Adem{\'a}s, la interfaz gr{\'a}fica de usuario incluida permite que usuarios sin experiencia con R accedan a las mismas funcionalidades.},
	isbn = {978-84-608-4099-2},
	author = {David Charte and Francisco Charte}
}
@article {288,
	title = {MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation},
	journal = {Knowledge-Based Systems},
	volume = {89},
	year = {2015},
	note = {TIN2012-33856,TIN2011-28488,P10-TIC-6858,P11-TIC-7765},
	pages = {385{\textendash}397},
	chapter = {385},
	abstract = {Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this paper the process of synthetic instance generation for multilabel datasets (MLDs) is studied and MLSMOTE (Multilabel Synthetic Minority Over-sampling Technique), a new algorithm aimed to produce synthetic instances for imbalanced MLDs, is proposed. An extensive review on how imbalance in the multilabel context has been tackled in the past is provided, along with a thorough experimental study aimed to verify the benefits of the proposed algorithm. Several multilabel classification algorithms and other multilabel oversampling methods are considered, as well as ensemble-based algorithms for imbalanced multilabel classification. The empirical analysis shows that MLSMOTE is able to improve the classification results produced by existent proposals.},
	doi = {10.1016/j.knosys.2015.07.019},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@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}
}
@conference {Gcgcj15,
	title = {Usando Algoritmos de Descubrimiento de Subgrupos en R: El Paquete SDR},
	booktitle = {VII Simposio de Teor{\'\i}a y Aplicaciones de Miner{\'\i}a de Datos},
	year = {2015},
	note = {TIN2012-33856},
	pages = {739-748},
	author = {A.M. Garc{\'\i}a-Vico and Francisco Charte and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@article {296,
	title = {Working with Multilabel Datasets in R: The mldr Package},
	journal = {The R Journal},
	volume = {7},
	number = {2},
	year = {2015},
	note = {TIN2012-33856},
	pages = {149{\textendash}162},
	abstract = {Most classification algorithms deal with datasets which have a set of input features, the variables to be used as predictors, and only one output class, the variable to be predicted. However, in late years many scenarios in which the classifier has to work with several outputs have come to life. Automatic labeling of text documents, image annotation or protein classification are among them. Multilabel datasets are the product of these new needs, and they have many specific traits. The mldr package allows the user to load datasets of this kind, obtain their characteristics, produce specialized plots, and manipulate them. The goal is to provide the exploratory tools needed to analyze multilabel datasets, as well as the transformation and manipulation functions that will make possible to apply binary and multiclass classification models to this data or the development of new multilabel classifiers. Thanks to its integrated user interface, the exploratory functions will be available even to non-specialized R users.},
	doi = {10.32614/RJ-2015-027},
	author = {Francisco Charte and David Charte}
}
@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 {302,
	title = {Propuesta de una asignatura de Dise{\~n}o de Servidores para la especialidad de Tecnolog{\'\i}as de Informaci{\'o}n},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {4},
	year = {2014},
	note = {-},
	pages = {15{\textendash}24},
	abstract = {This paper presents the subject Design and Deployment of Servers belonging to the Information Technologies of the Computer Science Engineering at the University of Ja{\'e}n. The objective of this subject is to provide the training established in the different competences related to the development and deployment of hardware infrastructures that supports the currently information systems. These information systems have characteristics such as ubiquitous access, high computational costs or high availability, among others. Thus, the subject addresses concepts from design of systems, monitoring, benchmarking or evaluation, to high availability, scalability or load balancing.},
	issn = {2173-8688},
	author = {A.J. Rivera-Rivas and Espinilla, Macarena and A. Fern{\'a}ndez and Santamar{\'\i}a L{\'o}pez, Jos{\'e} and Francisco Charte}
}
@conference {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}
}
@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 {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 {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}
}
@conference {326,
	title = {ReturnOK: El pasado de la computaci{\'o}n personal},
	booktitle = {V Congreso Internacional de Patrimonio e Historia de la Ingenier{\'\i}a},
	year = {2010},
	note = {-},
	month = {4},
	pages = {1{\textendash}19},
	address = {Las Palmas de Gran Canaria (Spain)},
	abstract = {La historia de la inform{\'a}tica o computaci{\'o}n es una parte de la historia humana y, al tiempo, una fuente de informaci{\'o}n indispensable sobre la evoluci{\'o}n del hardware y el software en las {\'u}ltimas d{\'e}cadas. Tambi{\'e}n resulta indispensable para entender el estado actual de las tecnolog{\'\i}as de la informaci{\'o}n en m{\'u}ltiples aspectos: el desarrollo de microprocesadores y la forma en que fueron surgiendo los sistemas digitales, el nacimiento de la microinform{\'a}tica personal (que represent{\'o} el acceso cuasi-universal a los ordenadores), el dise{\~n}o de software a trav{\'e}s de los lenguajes que fueron surgiendo, c{\'o}mo estos han ido adapt{\'a}ndose en cada momento y los actores m{\'a}s relevantes que han influido de manera decisiva en este campo. Con el fin de difundir la historia de los precursores de los PCs (los microcomputadores de los a{\~n}os 70 y 80) se ha creado una wiki tem{\'a}tica a la que se ha denominado ReturnOK: el museo digital del PC (http://museopc.ujaen.es). Este sitio web es el resultado de un proyecto de innovaci{\'o}n docente que se empez{\'o} a dise{\~n}ar y desarrollar en 2007. Desde esta wiki (un espacio colaborativo) cualquier aficionado a la retroinform{\'a}tica puede conocer la evoluci{\'o}n de la inform{\'a}tica y contribuir con sus conocimientos y recursos multimedia desde cualquier navegador Web. Esta wiki ofrece informaci{\'o}n relativa al hardware hist{\'o}rico (descripciones, fichas t{\'e}cnicas, fotograf{\'\i}as, etc.), el software utilizado en dicho hardware, emuladores que hacen posible ejecutarlos en dispositivos actuales (ordenadores y tel{\'e}fonos m{\'o}viles), biograf{\'\i}as, publicaciones de la {\'e}poca y bibliograf{\'\i}a relacionada. Se convierte, por tanto, en un medio de transferencia de conocimiento y de aprendizaje adicional a los ya cl{\'a}sicos, {\'u}til tanto para los estudios espec{\'\i}ficos de inform{\'a}tica como para otros afines: electr{\'o}nica, industrial, etc.},
	isbn = {84-920788-4},
	author = {Lina Garc{\'\i}a and Ildefonso Ruano and Francisco Charte and Andr{\'e}s Molina and Jos{\'e} R. Balsas}
}
@article {327,
	title = {ReturnOK, la Wiki sobre retroinform{\'a}tica},
	journal = {Iniciaci{\'o}n a la Investigaci{\'o}n},
	volume = {5},
	year = {2010},
	pages = {1-5},
	abstract = {En este art{\'\i}culo se describe el p{\'o}ster que resume las aportaciones del Proyecto de Innovaci{\'o}n Docente de la Universidad de Ja{\'e}n (convocatoria 2007-2009) que se denomin{\'o} "Museo digital sobre precursores del PC. Desarrollo, implementaci{\'o}n y explotaci{\'o}n de una wiki tem{\'a}tica". Fue financiado con 3000 euros por el Secretariado de Innovaci{\'o}n Docente dependiente del Vicerrectorado de Ordenaci{\'o}n Acad{\'e}mica, Innovaci{\'o}n Docente y Profesorado. Este proyecto ha sido galardonado en la I Convocatoria de premios a la Innovaci{\'o}n Docente de la Universidad de Ja{\'e}n 2010 con el Premio en el {\'A}rea de Tecnolog{\'\i}a.},
	issn = {1988-415X},
	author = {Lina Garc{\'\i}a Cabrera and Ildefonso Ruano and Francisco Charte and Andr{\'e}s Molina Aguilar and Jos{\'e} Ram{\'o}n Balsas Almagro}
}
