@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}
}
@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}
}
@article {292,
	title = {Dealing with difficult minority labels in imbalanced mutilabel data sets},
	journal = {Neurocomputing},
	volume = {326},
	year = {2019},
	note = {TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765},
	pages = {39{\textendash}53},
	abstract = {Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In addition, this characteristic challenges many of the existent preprocessing algorithms. Furthermore, the concurrence between imbalanced labels can make harder the learning from certain labels. These are what we call difficult labels. In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed. Specific metrics to assess this trait in multilabel datasets, called SCUMBLE (Score of ConcUrrence among iMBalanced LabEls) and SCUMBLELbl, are presented along with REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels), a new algorithm aimed to relax label concurrence. How to deal with this problem using the R mldr package is also outlined.},
	doi = {10.1016/j.neucom.2016.08.158},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {752,
	title = {Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ?},
	journal = {IEEE Computational Intelligence},
	volume = {1},
	number = {14},
	year = {2019},
	note = {TIN2015-68454-R; TIN2015-67661-P; TIN2017-89517-P},
	pages = {69-81},
	abstract = {Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data.},
	issn = {1556-603X},
	doi = {10.1109/TFUZZ.2018.2814577},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and O. Cord{\'o}n and F. Marcelloni and F. Herrera}
}
@article {293,
	title = {REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization},
	journal = {Neurocomputing},
	volume = {326},
	year = {2019},
	note = {TIN2014-57251-P,TIN2015-68454-R,P11-TIC-7765},
	pages = {110{\textendash}122},
	abstract = {The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification. A handful of multilabel resampling methods have been proposed in late years, aiming to balance the labels distribution. However, these methods have to face a new obstacle, specific for multilabel data, as is the joint appearance of minority and majority labels in the same data patterns. We presented recently a new algorithm designed to decouple imbalanced labels concurring in the same instance, called REMEDIAL (REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels). The goal of this work is to propose REMEDIAL-HwR (REMEDIAL Hybridization with Resampling), a procedure to hybridize this method with some of the best resampling algorithms available in the literature, including random oversampling, heuristic undersampling and synthetic sample generation techniques. These hybrid methods are then empirically analyzed, determining how their behavior is influenced by the label decoupling process. The analysis of results shows that the proposed method improves certain classifiers performance when it is applied over imbalanced datasets with label concurrence. In addition, a noteworthy set of guidelines on the combined use of these techniques can be drawn from the conducted experimentation.},
	doi = {10.1016/j.neucom.2017.01.118},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {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}
}
@conference {277,
	title = {A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2015-68454-R, TIN2017-89517-P},
	pages = {1316-1317},
	author = {A. Fern{\'a}ndez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {333,
	title = {A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines},
	booktitle = {XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (CAEPIA 2018)},
	year = {2018},
	note = {-},
	month = {10},
	pages = {949{\textendash}950},
	address = {Granada (Spain)},
	abstract = {This is a summary of our article published in Information Fusion to be part of the CAEPIA-18 KeyWorks.},
	isbn = {978-88-61970-00-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@article {Charte2018,
	title = {A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations},
	journal = {Progress in Artificial Intelligence},
	year = {2018},
	month = {Nov},
	abstract = {Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.},
	issn = {2192-6360},
	doi = {10.1007/s13748-018-00167-7},
	url = {https://doi.org/10.1007/s13748-018-00167-7},
	author = {David Charte and Francisco Charte and S. Garc{\'\i}a and F. Herrera}
}
@article {266,
	title = {A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy},
	journal = {Knowledge-Based Systems},
	volume = {139},
	year = {2018},
	note = {TIN2014-915 57251-P, TIN2015-68454-R},
	pages = {89-100},
	doi = {10.1016/j.knosys.2017.10.015},
	author = {C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {276,
	title = {Atipicidad: Medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas},
	booktitle = {Proc. of the XVIII Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial (XVIII CAEPIA)},
	year = {2018},
	note = {TIN2014-916 57251-P, TIN2015-68454-R},
	pages = {827-828},
	author = {C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@article {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 {312,
	title = {A first approach towards a fuzzy decision tree for multilabel classification},
	booktitle = {2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2017},
	note = {TIN2014- 57251-P,P11-TIC-7765},
	month = {7},
	pages = {1{\textendash}6},
	address = {Naples (Italy)},
	abstract = {This paper proposes a multilabel fuzzy decision tree classifier named FuzzDTML. The algorithm uses generalized fuzzy entropy, aggregated over all labels, to choose the best attribute for growing the tree. The proposed algorithm also can generate leaves predicting partial label sets, which can incorporate to some degree the dependence among labels, as well as produce more interpretable models. An empirical analysis shows that, although the algorithm does not yet incorporate pruning nor fuzzy interval adjustment phases, it is competitive with other tree based approaches for multilabel classification, with better performance in data sets having numerical features that can be fuzzified.},
	isbn = {978-1-5090-6034-4},
	doi = {10.1109/FUZZ-IEEE.2017.8015521},
	author = {Prati, Ronaldo C and Francisco Charte and F. Herrera}
}
@article {627,
	title = {A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets},
	journal = {International Journal of Neural Systems},
	volume = {27},
	year = {2017},
	note = {TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765, UJA2014/06/15},
	pages = {1-17},
	doi = {10.1142/S0129065717500289},
	author = {A. Fern{\'a}ndez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@article {753,
	title = {KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining.},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {10},
	number = {1},
	year = {2017},
	pages = {1238-1249},
	issn = {1875-6891},
	author = {I. Triguero and S. Gonzalez and J.M. Moyano and S. Garc{\'\i}a and J. Alcal{\'a}-Fdez and J. Luengo and A. Fern{\'a}ndez and M. J. del Jesus and L. S{\'a}nchez and F. Herrera and ATLANTIS PRESS.}
}
@article {599,
	title = {A View on Fuzzy Systems for Big Data: Progress and Opportunities},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {9},
	number = {1},
	year = {2016},
	note = {TIN2014-57251-P, P11-TIC-7765, UJA2014/06/15},
	pages = {69-80},
	author = {A. Fern{\'a}ndez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {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 {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 {287,
	title = {Addressing imbalance in multilabel classification: Measures and random resampling algorithms},
	journal = {Neurocomputing},
	volume = {163},
	year = {2015},
	note = {TIN2012-33856,TIN2011-28488,P10-TIC-6858,P11-TIC-7765},
	pages = {3{\textendash}16},
	chapter = {3},
	abstract = {The purpose of this paper is to analyze the imbalanced learning task in the multilabel scenario, aiming to accomplish two different goals. The first one is to present specialized measures directed to assess the imbalance level in multilabel datasets (MLDs). Using these measures we will be able to conclude which MLDs are imbalanced, and therefore would need an appropriate treatment. The second objective is to propose several algorithms designed to reduce the imbalance in MLDs in a classifier-independent way, by means of resampling techniques. Two different approaches to divide the instances in minority and majority groups are studied. One of them considers each label combination as class identifier, whereas the other one performs an individual evaluation of each label imbalance level. A random undersampling and a random oversampling algorithm are proposed for each approach, giving as result four different algorithms. All of them are experimentally tested and their effectiveness is statistically evaluated. From the results obtained, a set of guidelines directed to show when these methods should be applied is also provided.},
	doi = {10.1016/j.neucom.2014.08.091},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/978-3-319-24834-9_5,
	title = {Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection},
	booktitle = {Intelligent Data Engineering and Automated Learning {\textendash} IDEAL 2015},
	year = {2015},
	pages = {36{\textendash}44},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In classification tasks with imbalanced datasets the distribution of examples between the classes is uneven. However, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping between the classes is possibly the most significant one for a correct discrimination between the classes.},
	isbn = {978-3-319-24834-9},
	author = {Fern{\'a}ndez, Alberto and M. J. del Jesus and F. Herrera},
	editor = {Jackowski, Konrad and Burduk, Robert and Walkowiak, Krzysztof and Wozniak, Michal and Yin, Hujun}
}
@article {288,
	title = {MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation},
	journal = {Knowledge-Based Systems},
	volume = {89},
	year = {2015},
	note = {TIN2012-33856,TIN2011-28488,P10-TIC-6858,P11-TIC-7765},
	pages = {385{\textendash}397},
	chapter = {385},
	abstract = {Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this paper the process of synthetic instance generation for multilabel datasets (MLDs) is studied and MLSMOTE (Multilabel Synthetic Minority Over-sampling Technique), a new algorithm aimed to produce synthetic instances for imbalanced MLDs, is proposed. An extensive review on how imbalance in the multilabel context has been tackled in the past is provided, along with a thorough experimental study aimed to verify the benefits of the proposed algorithm. Several multilabel classification algorithms and other multilabel oversampling methods are considered, as well as ensemble-based algorithms for imbalanced multilabel classification. The empirical analysis shows that MLSMOTE is able to improve the classification results produced by existent proposals.},
	doi = {10.1016/j.knosys.2015.07.019},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {318,
	title = {QUINTA: A question tagging assistant to improve the answering ratio in electronic forums},
	booktitle = {IEEE International Conference on Computer as a Tool, EUROCON 2015},
	year = {2015},
	note = {TIN2014-57251-P,TIN2012-33856,P10-TIC-06858,P11-TIC-7765},
	month = {9},
	pages = {1-6},
	address = {Salamanca (Spain)},
	abstract = {The Web is broadly used nowadays to obtain information about almost any topic, from scientific procedures to cooking recipes. Electronic forums are very popular, with thousands of questions asked and answered every day. Correctly tagging the questions posted by users usually produces quicker and better answers by other users and experts. In this paper a prototype of a system aimed to assist the users while tagging their questions is proposed. To accomplish this task, firstly the text of each post is processed to produce a multilabel dataset, then a lazy nearest neighbor multilabel classification algorithm is used to predict the tags on new posts. The obtained results are promising, opening the door to the developing of a full automated system for this task.},
	isbn = {978-1-4799-8569-2},
	doi = {10.1109/EUROCON.2015.7313677},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@conference {319,
	title = {Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels},
	booktitle = {10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015},
	year = {2015},
	note = {TIN2011-28488,TIN2012-33856,P10-TIC-06858,P11-TIC-7765},
	month = {6},
	pages = {489{\textendash}501},
	address = {Bilbao (Spain)},
	abstract = {Multilabel classification is a task that has been broadly studied in late years. However, how to face learning from imbalanced multilabel datasets (MLDs) has only been addressed latterly. In this regard, a few proposals can be found in the literature, most of them based on resampling techniques adapted from the traditional classification field. The success of these methods varies extraordinarily depending on the traits of the chosen MLDs. One of the characteristics which significantly influences the behavior of multilabel resampling algorithms is the joint appearance of minority and majority labels in the same instances. It was demonstrated that MLDs with a high level of concurrence among imbalanced labels could hardly benefit from resampling methods. This paper proposes an original resampling algorithm, called REMEDIAL, which is not based on removing majority instances nor creating minority ones, but on a procedure to decouple highly imbalanced labels. As will be experimentally demonstrated, this is an interesting approach for certain MLDs.},
	isbn = {978-3-319-19643-5},
	doi = {10.1007/978-3-319-19644-2_41},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {FERNANDEZ2015109,
	title = {Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges},
	journal = {Knowledge-Based Systems},
	volume = {80},
	year = {2015},
	note = {25th anniversary of Knowledge-Based Systems},
	pages = {109 - 121},
	abstract = {Evolutionary Fuzzy Systems are a successful hybridization between fuzzy systems and Evolutionary Algorithms. They integrate both the management of imprecision/uncertainty and inherent interpretability of Fuzzy Rule Based Systems, with the learning and adaptation capabilities of evolutionary optimization. Over the years, many different approaches in Evolutionary Fuzzy Systems have been developed for improving the behavior of fuzzy systems, either acting on the Fuzzy Rule Base Systems{\textquoteright} elements, or by defining new approaches for the evolutionary components. All these efforts have enabled Evolutionary Fuzzy Systems to be successfully applied in several areas of Data Mining and engineering. In accordance with the former, a wide number of applications have been also taken advantage of these types of systems. However, with the new advances in computation, novel problems and challenges are raised every day. All these issues motivate researchers to make an effort in releasing new ways of addressing them with Evolutionary Fuzzy Systems. In this paper, we will review the progression of Evolutionary Fuzzy Systems by analyzing their taxonomy and components. We will also stress those problems and applications already tackled by this type of approach. We will present a discussion on the most recent and difficult Data Mining tasks to be addressed, and which are the latest trends in the development of Evolutionary Fuzzy Systems.},
	keywords = {Big Data, data mining, Evolutionary Fuzzy Systems, fuzzy rule based systems, Multi-Objective Evolutionary Fuzzy Systems, New trends, Scalability, Taxonomy},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2015.01.013},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705115000209},
	author = {Alberto Fernandez and Victoria L{\'o}pez and M. J. del Jesus and F. Herrera}
}
@article {Ltcgh14,
	title = {Addressing Imbalanced Classification with Instance Generation Techniques: IPADE-ID},
	journal = {Neurocomputing},
	volume = {126},
	year = {2014},
	note = {TIN2011-28488,P11-TIC-7765,P10-TIC-6858},
	pages = {15-28},
	doi = {10.1016/j.neucom.2013.01.050},
	author = {V. L{\'o}pez and I. Triguero and C. J. Carmona and S. Garc{\'\i}a and F. Herrera}
}
@article {doi:10.1002/widm.1134,
	title = {Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {4},
	number = {5},
	year = {2014},
	pages = {380-409},
	abstract = {The term {\textquoteleft}Big Data{\textquoteright} has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380{\textendash}409. doi: 10.1002/widm.1134 This article is categorized under: Technologies > Classification Technologies > Computer Architectures for Data Mining},
	issn = {1942-4787},
	doi = {10.1002/widm.1134},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1134},
	author = {Fern{\'a}ndez, Alberto and del R{\'\i}o, Sara and L{\'o}pez, Victoria and Bawakid, Abdullah and M. J. del Jesus and Benitez, Jose M. and F. Herrera}
}
@conference {321,
	title = {Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms},
	booktitle = {9th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2014)},
	year = {2014},
	note = {TIN2011-28488,TIN2012-33856,P10-TIC-06858,P11-TIC-9704},
	month = {6},
	pages = {110{\textendash}121},
	address = {Salamanca (Spain)},
	abstract = {In the context of multilabel classification, the learning from imbalanced data is getting considerable attention recently. Several algorithms to face this problem have been proposed in the late five years, as well as various measures to assess the imbalance level. Some of the proposed methods are based on resampling techniques, a very well-known approach whose utility in traditional classification has been proven. This paper aims to describe how a specific characteristic of multilabel datasets (MLDs), the level of concurrence among imbalanced labels, could have a great impact in resampling algorithms behavior. Towards this goal, a measure named SCUMBLE, designed to evaluate this concurrence level, is proposed and its usefulness is experimentally tested. As a result, a straightforward guideline on the effectiveness of multilabel resampling algorithms depending on MLDs characteristics can be inferred.},
	isbn = {978-3-319-07616-4},
	doi = {10.1007/978-3-319-07617-1_10},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {286,
	title = {LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {25},
	number = {10},
	year = {2014},
	note = {TIN2012-33856,TIN2011-28488,TIC-3928,P10-TIC-6858},
	pages = {1842-1854},
	chapter = {1842},
	abstract = {Multilabel classification (MLC) has generated considerable research interest in recent years, as a technique that can be applied to many real-world scenarios. To process them with binary or multiclass classifiers, methods for transforming multilabel data sets (MLDs) have been proposed, as well as adapted algorithms able to work with this type of data sets. However, until now, few studies have addressed the problem of how to deal with MLDs having a large number of labels. This characteristic can be defined as high dimensionality in the label space (output attributes), in contrast to the traditional high dimensionality problem, which is usually focused on the feature space (by means of feature selection) or sample space (by means of instance selection). The purpose of this paper is to analyze dimensionality in the label space in MLDs, and to present a transformation methodology based on the use of association rules to discover label dependencies. These dependencies are used to reduce the label space, to ease the work of any MLC algorithm, and to infer the deleted labels in a final postprocessing stage. The proposed process is validated in an extensive experimentation with several MLDs and classification algorithms, resulting in a statistically significant improvement of performance in some cases, as will be shown.},
	doi = {10.1109/TNNLS.2013.2296501},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {545,
	title = {METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems},
	journal = {Information Sciences},
	volume = {276},
	year = {2014},
	note = {P10-TIC-6858,TIN2011-28488, DPI2012-39381-C02-02,TIN2012-33856},
	pages = {63{\textendash}79},
	abstract = {In this contribution, we propose a two-stage method for Accurate Fuzzy Modeling in High-Dimensional Regression Problems using Approximate Takagi{\textendash}Sugeno{\textendash}Kang Fuzzy Rule-Based Systems. In the first stage, an evolutionary data base learning is performed (involving variables, granularities and slight fuzzy partition displacements) together with an inductive rule base learning within the same process. The second stage is a post-processing process to perform a rule selection and a scatter-based tuning of the membership functions for further refinement of the learned solutions. Moreover, the second stage incorporates an efficient Kalman filter to learn the coefficients of the consequent polynomial function in the Takagi{\textendash}Sugeno{\textendash}Kang rules. Both stages include mechanisms that significantly improve the accuracy of the model and ensure a fast convergence in high-dimensional and large-scale regression datasets. We tested our approach on 28 real-world datasets with different numbers of variables and instances. Five well-known methods have been executed as references. We compared the different approaches by applying non-parametric statistical tests for pair-wise and multiple comparisons. The results confirm the effectiveness of the proposed method, showing better results in accuracy within a reasonable computing time.},
	doi = {10.1016/j.ins.2014.02.047},
	author = {M. J. Gacto and M. Galende and R. Alcal{\'a} and F. Herrera}
}
@conference {320,
	title = {MLeNN: A First Approach to Heuristic Multilabel Undersampling},
	booktitle = {15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014},
	year = {2014},
	note = {TIN2011-28488,TIN2012-33856,P10-TIC-06858,P11-TIC-9704},
	month = {9},
	pages = {1-9},
	address = {Salamanca (Spain)},
	abstract = {Learning from imbalanced multilabel data is a challenging task that has attracted considerable attention lately. Some resampling algorithms used in traditional classification, such as random undersampling and random oversampling, have been already adapted in order to work with multilabel datasets. In this paper MLeNN (MultiLabel edited Nearest Neighbor), a heuristic multilabel undersampling algorithm based on the well-known Wilson{\textquoteright}s Edited Nearest Neighbor Rule, is proposed. The samples to be removed are heuristically selected, instead of randomly picked. The ability of MLeNN to improve classification results is experimentally tested, and its performance against multilabel random undersampling is analyzed. As will be shown, MLeNN is a competitive multilabel undersampling alternative, able to enhance significantly classification results.},
	isbn = {978-3-319-10839-1},
	doi = {10.1007/978-3-319-10840-7_1},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {Cgdh14,
	title = {Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {4},
	number = {2},
	year = {2014},
	note = {TIN2012-33856,TIN2010-15055,TIN2011-28488,TIC-3928,P11-TIC-7765,P10-TIC-6858},
	pages = {87-103},
	doi = {10.1002/widm.1118},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@conference {322,
	title = {A First Approach to Deal with Imbalance in Multi-label Datasets},
	booktitle = {8th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2013)},
	year = {2013},
	note = {TIN2012-33856,TIN2011-28488,TIC-3928,P10-TIC-6858},
	month = {9},
	pages = {150-160},
	address = {Salamanca (Spain)},
	abstract = {The process of learning from imbalanced datasets has been deeply studied for binary and multi-class classification. This problem also affects to multi-label datasets. Actually, the imbalance level in multi-label datasets uses to be much larger than in binary or multi-class datasets. Notwithstanding, the proposals on how to measure and deal with imbalanced datasets in multi-label classification are scarce. In this paper, we introduce two measures aimed to obtain information about the imbalance level in multi-label datasets. Furthermore, two preprocessing methods designed to reduce the imbalance level in multi-label datasets are proposed, and their effectiveness is validated experimentally. Finally, an analysis for determining when these methods have to be applied depending on the dataset characteristics is provided.},
	isbn = {978-3-642-40845-8},
	doi = {10.1007/978-3-642-40846-5_16},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {LOPEZ201385,
	title = {A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets},
	journal = {Knowledge-Based Systems},
	volume = {38},
	year = {2013},
	note = {Special Issue on "Advances in Fuzzy Knowledge Systems: Theory and Application"},
	pages = {85 - 104},
	abstract = {Lots of real world applications appear to be a matter of classification with imbalanced data-sets. This problem arises when the number of instances from one class is quite different to the number of instances from the other class. Traditionally, classification algorithms are unable to correctly deal with this issue as they are biased towards the majority class. Therefore, algorithms tend to misclassify the minority class which usually is the most interesting one for the application that is being sorted out. Among the available learning approaches, fuzzy rule-based classification systems have obtained a good behavior in the scenario of imbalanced data-sets. In this work, we focus on some modifications to further improve the performance of these systems considering the usage of information granulation. Specifically, a positive synergy between data sampling methods and algorithmic modifications is proposed, creating a genetic programming approach that uses linguistic variables in a hierarchical way. These linguistic variables are adapted to the context of the problem with a genetic process that combines rule selection with the adjustment of the lateral position of the labels based on the 2-tuples linguistic model. An experimental study is carried out over highly imbalanced and borderline imbalanced data-sets which is completed by a statistical comparative analysis. The results obtained show that the proposed model outperforms several fuzzy rule based classification systems, including a hierarchical approach and presents a better behavior than the C4.5 decision tree.},
	keywords = {Borderline examples, Fuzzy rule based classification systems, Genetic rule selection, Hierarchical fuzzy partitions, Imbalanced data-sets, Tuning},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2012.08.025},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705112002596},
	author = {Victoria L{\'o}pez and Alberto Fernandez and M. J. del Jesus and F. Herrera}
}
@article {FERNANDEZ201397,
	title = {Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches},
	journal = {Knowledge-Based Systems},
	volume = {42},
	year = {2013},
	pages = {97 - 110},
	abstract = {The imbalanced class problem is related to the real-world application of classification in engineering. It is characterised by a very different distribution of examples among the classes. The condition of multiple imbalanced classes is more restrictive when the aim of the final system is to obtain the most accurate precision for each of the concepts of the problem. The goal of this work is to provide a thorough experimental analysis that will allow us to determine the behaviour of the different approaches proposed in the specialised literature. First, we will make use of binarization schemes, i.e., one versus one and one versus all, in order to apply the standard approaches to solving binary class imbalanced problems. Second, we will apply several ad hoc procedures which have been designed for the scenario of imbalanced data-sets with multiple classes. This experimental study will include several well-known algorithms from the literature such as decision trees, support vector machines and instance-based learning, with the intention of obtaining global conclusions from different classification paradigms. The extracted findings will be supported by a statistical comparative analysis using more than 20 data-sets from the KEEL repository.},
	keywords = {Cost-sensitive learning, Imbalanced data-sets, Multi-classification, Pairwise learning, Preprocessing},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2013.01.018},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705113000300},
	author = {Alberto Fernandez and Victoria L{\'o}pez and Mikel Galar and M. J. del Jesus and F. Herrera and ELSEVIER SCIENCE BV}
}
@conference {544,
	title = {Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	note = {TIN2011-28488, DPI2009-14410-C02-02,P10-TIC-6858},
	month = {07/2013},
	pages = {1-7},
	abstract = {Abstract{\textemdash}This paper addresses the challenging problem of fuzzy modeling in high-dimensional and large scale regression datasets. To this end, we propose a scalable two-stage method for obtaining accurate fuzzy models in high-dimensional regression problems using approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, we propose an effective Multi-Objective Evolutionary Algorithm, based on an embedded genetic Data Base learning (involved variables, granularities and a slight lateral displacement of fuzzy partitions) together with an inductive rule base learning within the same process. The second stage is a post-processing process based on a second MOEA to perform a rule selection and a fine scatter-based tuning of the Membership Functions. Moreover, it incorporates an efficient Kalman filter to estimate the coefficients of the consequent polynomial functions in the Takagi-Sugeno-Kang rules. In both stages, we include mechanisms in order to significantly improve the accuracy of the model and to ensure a fast convergence in high-dimensional regression problems. The proposed method is compared to the classical ANFIS method and to a well-known evolutionary learning algorithm for obtaining accurate TSK systems in 8 datasets with different sizes and dimensions, obtaining better results.},
	author = {M. J. Gacto and M. Galende and R. Alcal{\'a} and F. Herrera}
}
@article {simidat193,
	title = {A Multi-Objective Evolutionary Algorithm for an Effective Tuning of Fuzzy Logic Controllers in Heating, Ventilating and Air Conditioning Systems},
	journal = {Applied Intelligence},
	volume = {36},
	number = {2},
	year = {2012},
	pages = {330-347},
	doi = {10.1007/s10489-010-0264-x},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat225,
	title = {A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms},
	booktitle = {1st International Conference on Pattern Recognition Applications and Methods (ICPRAM)},
	year = {2012},
	note = {TIN2011-28488,TIC-6858},
	month = {February},
	pages = {211-216},
	address = {Villamoura - (Portugal)},
	author = {S. Garc{\'\i}a and V. L{\'o}pez and J. Luengo and C. J. Carmona and F. Herrera}
}
@article {simidat232,
	title = {A Review on Ensembles for Class Imbalance Problem: Bagging, Boosting and Hybrid Based Approaches},
	journal = {IEEE Transactions on System, Man and Cybernetics - Part C: Applications and Reviews},
	volume = {42},
	number = {4},
	year = {2012},
	pages = {463-484},
	doi = {10.1109/TSMCC.2011.2161285},
	author = {M. Galar and A. Fern{\'a}ndez and E. Barrenechea and H. Bustince and F. Herrera}
}
@article {simidat231,
	title = {Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics},
	journal = {Expert Systems with Applications},
	volume = {39},
	number = {7},
	year = {2012},
	pages = {6585-6608},
	doi = {10.1016/j.eswa.2011.12.043},
	author = {V. L{\'o}pez and A. Fern{\'a}ndez and J.G. Moreno-Torres and F. Herrera}
}
@conference {simidat223,
	title = {Aprendizaje Evolutivo De Sistemas Aproximativos De Tipo TSK Para Problemas De Alta Dimensionalidad},
	booktitle = {XVI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2012},
	month = {February},
	pages = {295-300},
	address = {Valladolid (Spain)},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {inproceedings,
	title = {Cost Sensitive and Preprocessing for Classification with Imbalanced Data-sets: Similar Behaviour and Potential Hybridizations},
	booktitle = {ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods},
	volume = {2},
	year = {2012},
	month = {02},
	author = {L{\'o}pez, Victoria and Fern{\'a}ndez, Alberto and M. J. del Jesus and F. Herrera}
}
@article {Gdtch12,
	title = {Evolutionary-Based Selection of Generalized Instances for Imbalanced Classification},
	journal = {Knowledge-Based Systems},
	volume = {25},
	number = {1},
	year = {2012},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02},
	pages = {3-12},
	doi = {10.1016/j.knosys.2011.01.012},
	author = {S. Garc{\'\i}a and J. Derrac and I. Triguero and C. J. Carmona and F. Herrera}
}
@article {simidat233,
	title = {Feature Selection and Granularity Learning in Genetic Fuzzy Rule-Based Classication Systems for Highly Imbalanced Data-Sets},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {20},
	number = {3},
	year = {2012},
	pages = {369-397},
	doi = {S0218488512500195},
	author = {P. Villar and A. Fern{\'a}ndez and R. Carrasco and F. Herrera}
}
@conference {324,
	title = {Improving Multi-label Classifiers via Label Reduction with Association Rules},
	booktitle = {7th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2012)},
	year = {2012},
	note = {TIN2008-06681-C06-02,TIC-3928},
	month = {9},
	pages = {188{\textendash}199},
	address = {Salamanca (Spain)},
	abstract = {Multi-label classification is a generalization of well known problems, such as binary or multi-class classification, in a way that each processed instance is associated not with a class (label) but with a subset of these. In recent years different techniques have appeared which, through the transformation of the data or the adaptation of classic algorithms, aim to provide a solution to this relatively recent type of classification problem. This paper presents a new transformation technique for multi-label classification based on the use of association rules aimed at the reduction of the label space to deal with this problem.},
	isbn = {978-3-642-28930-9},
	doi = {10.1007/978-3-642-28931-6_18},
	author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {GarciaDCH12,
	title = {Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study},
	journal = {IEEE Transactions Pattern Analysis and Machiche Intelligence},
	volume = {34},
	number = {3},
	year = {2012},
	pages = {417{\textendash}435},
	doi = {10.1109/TPAMI.2011.142},
	author = {S. Garc{\'\i}a and J. Derrac and J. R. Cano and F. Herrera}
}
@article {simidat59,
	title = {Replacement Strategies to Preserve Useful Diversity in Steady-State Genetic Algorithms},
	journal = {Information Sciences},
	year = {2012},
	author = {M. Lozano and F. Herrera and J. R. Cano}
}
@conference {inproceedings,
	title = {Un sistema de clasificaci{\'o}n basado en reglas difusas jer{\'a}rquico con programaci{\'o}n gen{\'e}tica para problemas de clasificaci{\'o}n altamente no balanceados},
	year = {2012},
	month = {02},
	author = {L{\'o}pez, Victoria and Fern{\'a}ndez, Alberto and M. J. del Jesus and F. Herrera}
}
@conference {simidat217,
	title = {A double axis classi},
	booktitle = {9th International Workshop on Fuzzy Logic and Applications (WILF)},
	year = {2011},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {10.1007/978-3-642-23713-3_13,
	title = {A Double Axis Classification of Interpretability Measures for Linguistic Fuzzy Rule-Based Systems},
	booktitle = {Fuzzy Logic and Applications},
	year = {2011},
	pages = {99{\textendash}106},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we present a simple classification of the papers devoted to interpretability of Linguistic Fuzzy Rule-Based Systems attending to the type of interpretability measures and the part of the system for which they are applied, i.e., a double axis classification. A taxonomy considering this double axis is used to easily categorize the proposals in the existing literature. In this way, this work also represents a simple summary of the current state-of-the-art to assess the interpretability of Linguistic Fuzzy Rule-Based Systems.},
	isbn = {978-3-642-23713-3},
	author = {M. J. Gacto and Alcal{\'a}, R. and F. Herrera},
	editor = {Fanelli, Anna Maria and Pedrycz, Witold and Petrosino, Alfredo}
}
@article {simidat215,
	title = {A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {19},
	number = {4},
	year = {2011},
	pages = {666-681},
	doi = {10.1109/TFUZZ.2011.2131657 COMPLEMENTARY MATERIAL to the paper here: dataset partitions, results, figures, etc..},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@article {simidat235,
	title = {A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position},
	journal = {International Journal of Approximate Reasoning},
	volume = {52},
	number = {6},
	year = {2011},
	pages = {751-766},
	doi = {10.1016/j.ijar.2011.0},
	author = {J. Sanz and A. Fern{\'a}ndez and H. Bustince and F. Herrera}
}
@article {simidat238,
	title = {Addressing Data Complexity for Imbalanced Data Sets: Analysis of SMOTE-based Oversampling and Evolutionary Undersampling},
	journal = {Soft Computing},
	volume = {15},
	number = {10},
	year = {2011},
	pages = {1909-1936},
	doi = {10.1007/s00500-010-0625-8},
	author = {J. Luengo and A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera}
}
@article {Hcgd11,
	title = {An overview on Subgroup Discovery: Foundations and Applications},
	journal = {Knowledge and Information Systems},
	volume = {29},
	number = {3},
	year = {2011},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {495-525},
	doi = {10.1007/s10115-010-0356-2},
	author = {F. Herrera and C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {simidat221,
	title = {Analysis of the Impact of Using Different Diversity Functions for the Subgroup Discovery Algorithm NMEEF-SD},
	booktitle = {5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2011},
	month = {April},
	pages = {17-23},
	address = {Paris (France)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@conference {simidat205,
	title = {Evolutionary Multi-Objective Algorithm to Effectively Improve the Performance of the Classic Tuning of Fuzzy Logic Controllers for a Heating, Ventilating and Air Conditioning System},
	booktitle = {5th International Workshop On Genetic And Evolutionary Fuzzy Systems (GEFS)},
	year = {2011},
	month = {April},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {Dglch11,
	title = {Evolutionary Selection of Hyperrectangles in Nested Generalized Exemplar Learning},
	journal = {Applied Soft Computing},
	volume = {11},
	number = {3},
	year = {2011},
	note = {TIN2008-06681-C06-01},
	pages = {3032-3045},
	doi = {10.1016/j.asoc.2010.11.030},
	author = {S. Garc{\'\i}a and J. Derrac and J. Luengo and C. J. Carmona and F. Herrera}
}
@article {simidat216,
	title = {Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures},
	journal = {Information Sciences},
	volume = {181},
	number = {20},
	year = {2011},
	pages = {4340{\textendash}4360},
	doi = {10.1016/j.ins.2011.02.021 COMPLEMENTARY MATERIAL to the paper here: links to the papers with doi, new contributions, etc..},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat237,
	title = {KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework},
	journal = {Journal of Multiple-Valued Logic and Soft Computing},
	volume = {17},
	number = {2-3},
	year = {2011},
	pages = {255-287},
	author = {J. Alcal{\'a}-Fdez and A. Fern{\'a}ndez and J. Luengo and J. Derrac and S. Garc{\'\i}a and L. S{\'a}nchez and F. Herrera}
}
@conference {simidat206,
	title = {A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification},
	booktitle = {Twenty Third International Conference on Industrial, Engineering \& Other Applications of Applied Intelligent Systems (IEA/AIE)},
	year = {2010},
	note = {TIN2008-06681-C06-01,TIN2008-06681-C06-02},
	pages = {601-610},
	address = {Cordoba},
	author = {S. Garc{\'\i}a and J. Derrac and I. Triguero and C. J. Carmona and F. Herrera}
}
@inbook {GarciaCH10,
	title = {A Review on Evolutionary Prototype Selection},
	booktitle = {Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications},
	year = {2010},
	pages = {92{\textendash}113},
	doi = {10.4018/978-1-60566-798-0.ch005},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@article {simidat187,
	title = {Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power},
	journal = {Information Sciences},
	volume = {180},
	year = {2010},
	pages = {2044{\textendash}2064},
	doi = {10.1016/j.ins.2009.12.010},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and J. Luengo and F. Herrera}
}
@conference {simidat190,
	title = {Analysing the Hierarchical Fuzzy Rule Based Classification Systems with Genetic Rule Selection},
	booktitle = {4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2010},
	month = {March},
	pages = {69-74},
	address = {Mieres (Spain)},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@conference {simidat192,
	title = {Analysis of the Performance of a Semantic Interpretability-Based Tuning and Rule Selection of Fuzzy Rule-Based Systems by Means of a Multi-Objective Evolutionary Algorithm},
	booktitle = {LNAI 6097},
	year = {2010},
	month = {June},
	pages = {228-238},
	address = {C{\'o}rdoba},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat239,
	title = {Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {14},
	number = {6},
	year = {2010},
	pages = {913-941},
	doi = {10.1109/TEVC.2009.2039140},
	author = {A. Fern{\'a}ndez and J. Luengo and S. Garc{\'\i}a and E. Bernad{\'o}-Mansilla and F. Herrera}
}
@article {BERLANGA20101183,
	title = {GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems},
	journal = {Information Sciences},
	volume = {180},
	number = {8},
	year = {2010},
	pages = {1183 - 1200},
	abstract = {In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.},
	keywords = {classification, Fuzzy rule-based systems, Genetic Fuzzy Systems, Genetic programming, High-dimensional problems, Interpretability-accuracy trade-off},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2009.12.020},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025509005635},
	author = {F.J. Berlanga and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {simidat185,
	title = {Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning},
	journal = {Information Sciences},
	volume = {180},
	number = {19},
	year = {2010},
	pages = {3674-3685},
	doi = {10.1016/j.ins.2010.06.018},
	author = {J. Sanz and A. Fern{\'a}ndez and H. Bustince and F. Herrera}
}
@conference {simidat174,
	title = {Indice de Interpretabilidad Sem{\'a}ntica para el Ajuste de Sistemas Basados en Reglas Difusas y Selecci{\'o}n de Reglas Mediante un Algoritmo Evolutivo Multi-Objetivo},
	booktitle = {XV Edici{\'o}n del Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2010},
	month = {February},
	pages = {73-78},
	address = {Huelva},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat173,
	title = {Integration of an Index to Preserve the Semantic Interpretability in the Multi-Objective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {18},
	number = {3},
	year = {2010},
	pages = {515-531},
	doi = {10.1109/TFUZZ.2010.2041008},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {10.1007/978-3-642-13022-9_21,
	title = {Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market},
	booktitle = {Trends in Applied Intelligent Systems},
	year = {2010},
	pages = {205{\textendash}214},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlighted. In this paper an adaptation of CO2RBFN, evolutionary COoperative-COmpetitive algorithm for Radial Basis Function Networks design, applied to the long-term prediction of the extra-virgin olive oil price is presented. This long-term horizon has been fixed to six months. The results of CO2RBFN have been compared with other data mining methods, typically used in time series forecasting, such as other neural networks models, a support vector machine method and a fuzzy system.},
	isbn = {978-3-642-13022-9},
	author = {M.D. P{\'e}rez-Godoy and P{\'e}rez, Pedro and A.J. Rivera-Rivas and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Garc{\'\i}a-Pedrajas, Nicol{\'a}s and F. Herrera and Fyfe, Colin and Ben{\'\i}tez, Jos{\'e} Manuel and Ali, Moonis}
}
@inbook {inbook,
	title = {Introduction to the Experimental Design in the Data Mining Tool KEEL},
	year = {2010},
	month = {01},
	pages = {1-25},
	isbn = {978-1-61520-757-2},
	author = {Alcala-Fdez, Jesus and Garcia, S and S{\'a}nchez, Luciano and Robles, I and M. J. del Jesus and Bernado-Mansilla, E and Peregrin, Antonio and F. Herrera}
}
@conference {simidat191,
	title = {Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning},
	booktitle = {13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	series = {Lecture Notes on Artificial Intelligence},
	volume = {6178},
	year = {2010},
	month = {June},
	pages = {89-98},
	address = {Dortmund (Germany)},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {Cgdh10,
	title = {NMEEF-SD: Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {18},
	number = {5},
	year = {2010},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {958-970},
	doi = {10.1109/TFUZZ.2010.2060200},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {simidat186,
	title = {On the 2-Tuples Based Genetic Tuning Performance for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets},
	journal = {Information Sciences},
	volume = {180},
	number = {8},
	year = {2010},
	pages = {1268-1291},
	doi = {10.1016/j.ins.2009.12.014},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {simidat184,
	title = {Solving Multi-Class Problems with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning and Preference Relations},
	journal = {Fuzzy Sets and Systems},
	volume = {161},
	number = {23},
	year = {2010},
	pages = {3064-3080},
	doi = {10.1016/j.fss.2010.05.016},
	author = {A. Fern{\'a}ndez and M. Calder{\'o}n and E. Barrenechea and H. Bustince and F. Herrera}
}
@conference {simidat151,
	title = {A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability},
	booktitle = {Proceedings of the Joint International Fuzzy Systems Association World Congress and the European Society for Fuzzy Logic and Technology Conference (IFSA)},
	year = {2009},
	month = {July},
	pages = {1146-1151},
	publisher = {IFSA/EUSFLAT 2009},
	organization = {IFSA/EUSFLAT 2009},
	address = {Lisbon, Portugal},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {10.1007/978-3-642-02478-8_8,
	title = {A Preliminar Analysis of CO2RBFN in Imbalanced Problems},
	booktitle = {Bio-Inspired Systems: Computational and Ambient Intelligence},
	year = {2009},
	pages = {57{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs applied to classification problems on imbalanced domains and to study the cooperation of a well known preprocessing method, the {\textquoteleft}{\textquoteleft}Synthetic Minority Over-sampling Technique{\textquoteright}{\textquoteright} (SMOTE) with our algorithm. The good performance of CO2RBFN is shown through an experimental study carried out over a large collection of imbalanced data-sets.},
	isbn = {978-3-642-02478-8},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fern{\'a}ndez, A. and M. J. del Jesus and F. Herrera},
	editor = {Cabestany, Joan and Sandoval, Francisco and Prieto, Alberto and Corchado, Juan M.}
}
@article {simidat98,
	title = {Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems},
	journal = {Soft Computing},
	volume = {13},
	number = {5},
	year = {2009},
	pages = {419-436},
	doi = {10.1007/s00500-008-0359-z},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat199,
	title = {Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5},
	booktitle = {9th International Conference on Intelligent Systems Designs and Applications (ISDA)},
	year = {2009},
	pages = {523-528},
	author = {J. Luengo and A. Fern{\'a}ndez and F. Herrera and S. Garc{\'\i}a}
}
@conference {simidat113,
	title = {Algoritmo Gen{\'e}tico Multi-Objetivo Avanzado para el ajuste de un sistema difuso aplicado al Control de Sistemas de Ventilaci{\'o}n, Calefacci{\'o}n y Aire Acondicionado},
	booktitle = {Proceedings of the Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {595-602},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat156,
	title = {An analysis of evolutionary algorithms with different types of fuzzy rules in subgroup discovery},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2009},
	note = {TIN2008-06681-C06-01,TIN2008-06681-C06-02,TIC-3928},
	month = {August},
	pages = {1706-1711},
	address = {ICC Jeju, Jeju Island, Korea},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {simidat61,
	title = {Diagnose of Effective Evolutionary Prototype Selection using an Overlapping Measure},
	journal = {International Journal of Pattern Recognition and Artificial Intelligence},
	volume = {23},
	number = {8},
	year = {2009},
	pages = {1527-1548},
	author = {S. Garc{\'\i}a and J. R. Cano and E. Bernad{\'o}-Mansilla and F. Herrera}
}
@article {simidat145,
	title = {Enhancing the Effectiveness and Interpretability of Decision Tree and Rule Induction Classifiers with Evolutionary Training Set Selection over Imbalanced Problems},
	journal = {Applied Soft Computing},
	volume = {9},
	year = {2009},
	pages = {1304-1314},
	doi = {10.1016/j.asoc.2009.04.004},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and F. Herrera}
}
@article {simidat47,
	title = {Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data},
	journal = {Expert Systems with Applications},
	volume = {36},
	year = {2009},
	pages = {1632-1644},
	author = {C. Romero and P. Gonz{\'a}lez and S. Ventura and M. J. del Jesus and F. Herrera}
}
@conference {simidat176,
	title = {Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets},
	booktitle = {13 th International Fuzzy Systems Association World Congress and 6th European Society for Fuzzy Logic and Tecnology Conference (IFSA-EUSFLAT)},
	year = {2009},
	pages = {42-47},
	address = {Lisbon (Portugal)},
	author = {A. Fern{\'a}ndez and F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@conference {simidat170,
	title = {Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm},
	booktitle = {9th International Conference on Intelligent Systems Design and Applications (ISDA)},
	year = {2009},
	month = {November},
	pages = {109-114},
	address = {Pisa (Italy)},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat158,
	title = {Hierarchical fuzzy rule based classfication systems with genetic rule selection for imbalanced data-sets},
	journal = {International Journal of Approximate Reasoning},
	volume = {50},
	year = {2009},
	pages = {561-577},
	doi = {10.1016/j.ijar.2008.11.004},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {simidat100,
	title = {Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems},
	journal = {Applied Intelligence},
	volume = {31},
	number = {1},
	year = {2009},
	pages = {15-30},
	doi = {10.1007/s10489-007-0107-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat189,
	title = {Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-sets Using an Evolutionary Adaptive Inference System},
	booktitle = {10th International Work-Conference on Artificial Neural Networks (IWANN)},
	series = {Lecture Notes on Computer Science},
	volume = {5517},
	year = {2009},
	month = {June},
	pages = {294-301,},
	address = {Salamanca (Spain)},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and F. Herrera}
}
@article {simidat95,
	title = {KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems},
	journal = {Soft Computing},
	volume = {13},
	number = {3},
	year = {2009},
	pages = {307-318},
	doi = {10.1007/s00500-008-0323-y},
	author = {J. Alcal{\'a}-Fdez and L. S{\'a}nchez and S. Garc{\'\i}a and M. J. del Jesus and S. Ventura and J.M. Garrell and J. Otero and C. Romero and J. Bacardit and V. M. Rivas and J.C. Fern{\'a}ndez and F. Herrera}
}
@article {simidat99,
	title = {Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms},
	journal = {Fuzzy Sets and Systems},
	volume = {160},
	number = {7},
	year = {2009},
	pages = {905-921},
	doi = {10.1016/j.fss.2008.05.012},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@article {606,
	title = {Modelo predictivo colaborativo de apoyo al diagn{\'o}stico en servicio de urgencias psiqui{\'a}tricas},
	journal = {Revista Ib{\'e}rica de Sistemas y Tecnolog{\'\i}as de la informaci{\'o}n},
	volume = {4},
	number = {4},
	year = {2009},
	pages = {29-42},
	issn = {1646-9895},
	author = {J. R. Cano and P. Gonz{\'a}lez and Jos{\'e} Aguilera and A.G. L{\'o}pez-Herrera and F. Herrera and M. Nav{\'\i}o and Jim{\'e}nez-Arriero Miguel Angel}
}
@conference {simidat148,
	title = {Non-dominated Multi-objective Evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery},
	booktitle = {Proceedings of the Fourth International Conference on Hybrid Artificial Intelligence Systems (HAIS)},
	series = {LNAI},
	volume = {5572},
	year = {2009},
	note = {TIN2008-06681-C06-01,TIN2008-06681-C06-02,TIC-3928},
	month = {June},
	pages = {573-580},
	publisher = {Springer},
	organization = {Springer},
	address = {Salamanca (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@article {simidat157,
	title = {On the influence of an adaptive inference system in fuzzy rule-based classification sytems for imbalanced data-sets},
	journal = {Expert Systems with Applications},
	volume = {36},
	number = {6},
	year = {2009},
	pages = {9805-9812},
	doi = {10.1016/j.eswa.2009.02.048},
	author = {A. Fern{\'a}ndez and F. Herrera and M. J. del Jesus}
}
@conference {simidat93,
	title = {Un Primer Estudio sobre la Utilizaci{\'o}n de Selecci{\'o}n Evolutiva de Conjuntos de Entrenamiento en Problemas de Clasificaci{\'o}n con Clases no Balanceadas y {\'A}rboles de Decisi{\'o}n},
	booktitle = {Proceedings of VI Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {183-190},
	address = {M{\'a}laga (Spain)},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and F. Herrera}
}
@article {simidat57,
	title = {A memetic algorithm for Evolutionary Prototype Selection: A Scaling Up Approach},
	journal = {Pattern Recognition},
	volume = {41},
	number = {8},
	year = {2008},
	pages = {2693-2709},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@conference {simidat43,
	title = {A Novel Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for High Dimensional Problems},
	booktitle = {3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)},
	year = {2008},
	pages = {101-106},
	address = {WittenBommerholz (Germany)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@article {simidat85,
	title = {A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets},
	journal = {Fuzzy Sets and Systems},
	volume = {159},
	number = {18},
	year = {2008},
	pages = {2378-2398},
	issn = {0165-0114},
	doi = {10.1016/j.fss.2007.12.023},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@conference {simidat111,
	title = {An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy System},
	booktitle = {Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	year = {2008},
	month = {June},
	pages = {1121-1128},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat82,
	title = {Estudio de la influencia de las medidas de complejidad de los datos en los Sistemas de Clasifcaci{\'o}n Basados en Reglas Difusas: An{\'a}lisis de la Raz{\'o}n Discriminante de Fisher},
	booktitle = {XIV Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2008},
	month = {September},
	pages = {257-263},
	address = {Mieres (Spain)},
	author = {J. Luengo and S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@article {article,
	title = {Evolutionary Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules},
	year = {2008},
	month = {01},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel}
}
@conference {simidat52,
	title = {Influencia de la granularidad y de las medidas de calidad en SDIGA},
	booktitle = {XIV Congreso Espa{\~n}ol de Tecnolog{\'\i}as y L{\'o}gica Difusa( ESTYLF)},
	year = {2008},
	note = {TIN2005-08386-C05-01,TIN2005-08386-C05-03},
	month = {September},
	address = {Langreo-Mieres (Spain)},
	author = {P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus and F. Herrera}
}
@conference {simidat44,
	title = {KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems},
	booktitle = {3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS)},
	year = {2008},
	note = {TIN-2008-06681-C06-01,TIN-2008-06681-C06-02,TIC-3928},
	pages = {83-88},
	address = {WittenBommerholz (Germany)},
	author = {J. Alcal{\'a}-Fdez and S. Garc{\'\i}a and F. J. Berlanga and A. Fern{\'a}ndez and L. S{\'a}nchez and M. J. del Jesus and F. Herrera}
}
@article {610,
	title = {Making CN1 -SD Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection},
	journal = {Expert System with Applications},
	volume = {35},
	number = {4},
	year = {2008},
	pages = {1949-1965},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel and Garc{\'\i}a, Salvador}
}
@article {simidat58,
	title = {Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection},
	journal = {Expert Systems with Applications},
	volume = {35},
	year = {2008},
	pages = {1949-1965},
	author = {J. R. Cano and F. Herrera and M. Lozano and S. Garc{\'\i}a}
}
@conference {simidat112,
	title = {Multi-Objective Genetic Fuzzy Systems: On the Necessity of Including Expert Knowledge in the MOEA Design Process},
	booktitle = {Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	year = {2008},
	month = {June},
	pages = {1446-1453},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@inbook {simidat110,
	title = {On the use of Multiobjective Genetic Algorithms to Improve the Accuracy-Interpretability Trade-Off of Fuzzy Rule-Based Systems},
	booktitle = {Multi-objective Evolutionary Algorithms for Knowledge Discovery from Data Bases},
	volume = {98},
	year = {2008},
	isbn = {978-3-540-77466-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {A. Ghosh and S. Dehuri and S. Ghosh}
}
@inbook {Alcal{\'a}2008,
	title = {On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection},
	booktitle = {Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases},
	year = {2008},
	pages = {91{\textendash}107},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In the last years, multi-objective genetic algorithms have been successfully applied to obtain Fuzzy Rule-Based Systems satisfying different objectives, usually different performance measures.},
	isbn = {978-3-540-77467-9},
	doi = {10.1007/978-3-540-77467-9_5},
	url = {https://doi.org/10.1007/978-3-540-77467-9_5},
	author = {Alcal{\'a}, R. and Alcal{\'a}-Fdez, J. and M. J. Gacto and F. Herrera},
	editor = {Ghosh, Ashish and Dehuri, Satchidananda and Ghosh, Susmita}
}
@article {LozanoHC08,
	title = {Replacement strategies to preserve useful diversity in steady-state genetic algorithms},
	journal = {Information Sciences},
	volume = {178},
	number = {23},
	year = {2008},
	pages = {4421{\textendash}4433},
	doi = {10.1016/j.ins.2008.07.031},
	author = {M. Lozano and J. R. Cano and F. Herrera}
}
@article {simidat87,
	title = {Subgroup Discovery in Large Size Data Sets Preprocessed Using Stratified Instance Selection for Increasing the Presence of Minority Classes},
	journal = {Pattern Recognition Letters},
	volume = {29},
	year = {2008},
	pages = {2156-2164},
	doi = {10.1016/j.patrec.2008.08.001},
	author = {J. R. Cano and S. Garc{\'\i}a and F. Herrera}
}
@inbook {Jesus2008,
	title = {Subgroup Discovery with Linguistic Rules},
	booktitle = {Fuzzy Sets and Their Extensions: Representation, Aggregation and Models},
	year = {2008},
	pages = {411{\textendash}430},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest. Subgroup discovery algorithms aim at discovering individual rules, which must be represented in explicit symbolic form and which must be simple and understandable in order to be recognized as actionable by potential users.},
	isbn = {978-3-540-73723-0},
	doi = {10.1007/978-3-540-73723-0_21},
	url = {https://doi.org/10.1007/978-3-540-73723-0_21},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera},
	editor = {Bustince, Humberto and F. Herrera and Montero, Javier}
}
@article {simidat97,
	title = {A Multi-objectiveGenetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {15},
	number = {5},
	year = {2007},
	pages = {539{\textendash}557},
	doi = {10.1142/S0218488507004868},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera and J. Alcal{\'a}-Fdez}
}
@conference {simidat74,
	title = {A study on the Use of the Fuzzy Reasoning Method based on the Winning Rule Vs. Voting Procedure for Classification with Imbalanced Data Sets},
	booktitle = {Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN)},
	series = {Lecture Notes on Computer Science 4507},
	year = {2007},
	month = {June},
	pages = {375-382},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {San Sebasti{\'a}n (Spain)},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@conference {simidat75,
	title = {An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets},
	booktitle = {International Workshop on Fuzzy Logic and Applications (WILF)},
	series = {Lecture Notes in Computer Science 4578},
	year = {2007},
	month = {July},
	pages = {170-179},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Genova (Italy)},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and M. J. del Jesus and F. Herrera}
}
@conference {simidat54,
	title = {An{\'a}lisis of Evolutionary Prototype Selection by means of a Data Complexity Measure based on Class Separabilty},
	booktitle = {Actas del Taller de Miner{\'\i}a de Datos y Aprendizaje (TAMIDA)},
	year = {2007},
	pages = {145-152},
	address = {Zaragoza},
	author = {J. R. Cano and S. Garc{\'\i}a and F. Herrera and E. Bernad{\'o}-Mansilla}
}
@conference {simidat40,
	title = {Aplicaci{\'o}n de Algoritmos Evolutivos de Descubrimiento de Subgrupos en e-Learning: un Caso de Estudio},
	booktitle = {V Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2007},
	pages = {493-500},
	address = {Tenerife (Spain)},
	author = {C. Romero and P. Gonz{\'a}lez and S. Ventura and M. J. del Jesus and F. Herrera}
}
@conference {simidat109,
	title = {Aprendizaje Evolutivo de los Contextos de las Funciones de Pertenencia para Extraer Reglas de Asociaci{\'o}n Difusas},
	booktitle = {Proceedings of the II Congreso Espa{\~n}ol de Inform{\'a}tica (CEDI 2007). II Simposio sobre L{\'o}gica Fuzzy y Soft Computing (LFSC)},
	year = {2007},
	month = {September},
	pages = {25-32},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@article {simidat41,
	title = {Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {15},
	number = {4},
	year = {2007},
	pages = {578-592},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@article {simidat56,
	title = {Evolutionary Stratified Training Set Selection for Extracting Classification Rules with trade off Precision-Interpretability},
	journal = {Data \& Knowledge Engineering},
	volume = {60},
	number = {1},
	year = {2007},
	pages = {90-108},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {simidat107,
	title = {Genetic Learning of Membership Functions for Mining Fuzzy Association Rules},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1538-1543},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat49,
	title = {Multiobjective genetic algorithm for extractiong subgroup discovery fuzzy rules},
	booktitle = {2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (IEEE MCDM)},
	year = {2007},
	pages = {50-57},
	publisher = {Omnipress},
	organization = {Omnipress},
	address = {Honolulu (USA)},
	author = {P. Gonz{\'a}lez and M. J. del Jesus and F. Herrera}
}
@conference {4295638,
	title = {Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {In the design of fuzzy rule-based classification systems (FRBCSs) a feature selection process which determines the most relevant features is a crucial component in the majority of the classification problems. This simplification process increases the efficiency of the design process, improves the interpretability of the FRBCS obtained and its generalization capacity. Most of the feature selection algorithms provide a set of variables which are adequate for the induction process according to different quality measures. Nevertheless it can be useful for the induction process to determine not only a set of variables but also different set of variables. These sets of variables can be used for the design of a set of FRBCSs which can be combined in a multiclassifler system, improving the prediction capacity increasing its description capacity. In this work, different proposals of niching genetic algorithms for the feature selection process are analyzed. The different sets of features provided by them are used in a multiclassifier system designed by means of a genetic proposal. The experimentation shows the adaptation of this type of genetic algorithms to the FRBCS design.},
	keywords = {Algorithm design and analysis, classification, data mining, Databases, description capacity, Feature extraction, feature selection algorithms, Fuzzy reasoning, fuzzy rule-based classification systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithms, induction process, Knowledge representation, multiclassifler system, niching genetic algorithms, prediction capacity, Process design, Proposals},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295638},
	author = {Jos{\'e} Aguilera and M. Chica and M. J. del Jesus and F. Herrera}
}
@article {article,
	title = {Preface: Special Issue on Genetic Fuzzy Systems and the Interpretability{\textendash}Accuracy Trade-off},
	journal = {International Journal of Approximate Reasoning},
	volume = {44},
	year = {2007},
	month = {01},
	pages = {1-3},
	doi = {10.1016/j.ijar.2006.06.002},
	author = {Casillas, Jorge and F. Herrera and P{\'e}rez, Ra{\'u}l and Villar, P}
}
@article {simidat96,
	title = {Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation},
	journal = {Soft Computing},
	volume = {11},
	number = {5},
	year = {2007},
	pages = {401-419},
	doi = {10.1007/s00500-006-0106-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {757,
	title = {Special issue on genetic fuzzy systems and the interpretability-accuracy trade-off},
	journal = {International Journal of Approximate Reasoning},
	volume = {44},
	year = {2007},
	pages = {1-3},
	issn = {0888-613X},
	doi = {10.1016/J.IJAR.2006.06.002},
	author = {J. Casillas and F. Herrera and F.G.R. P{\'e}rez and M. J. del Jesus and P. Villar}
}
@conference {simidat77,
	title = {Statistical Comparisons by Means of Non-Parametric Tests: A Case Study on Genetic Based Machine Learning},
	booktitle = {Proceedings of the II Congreso Espa{\~n}ol de Inform{\'a}tica (CEDI 2007). V Taller Nacional de Miner{\'\i}a de Datos y Aprendizaje (TAMIDA)},
	year = {2007},
	month = {September},
	pages = {95-104},
	address = {Zaragoza (Spain)},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and A.D. Ben{\'\i}tez and F. Herrera}
}
@conference {simidat55,
	title = {Un algoritmo mem{\'e}tico para la selecci{\'o}n de prototipos: Una propuesta eficiente para problemas de tama{\~n}o medio},
	booktitle = {Proceedings Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2007},
	address = {Tenerife},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@conference {inproceedings,
	title = {Un estudio sobre el uso de algoritmos gen{\'e}ticos multimodales para selecci{\'o}n de caracter{\'\i}sticas},
	year = {2007},
	month = {02},
	author = {Jos{\'e} Aguilera and , ~J.~J and Chica, Manuel and M. J. del Jesus and , ~M.~J and F. Herrera and , ~F}
}
@conference {simidat70,
	title = {A first study on the use of fuzzy rule based classification systems for problems with imbalanced data sets},
	booktitle = {Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS)},
	year = {2006},
	month = {September},
	pages = {63-72},
	address = {Magdeburg (Germany)},
	author = {M. J. del Jesus and A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera}
}
@conference {simidat37,
	title = {A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems},
	booktitle = {The Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC)},
	series = {LNCS},
	volume = {4029},
	year = {2006},
	pages = {182-191},
	publisher = {Springer},
	organization = {Springer},
	address = {Zakopane (Poland)},
	author = {F. J. Berlanga and M. J. del Jesus and M. J. Gacto and F. Herrera}
}
@conference {simidat34,
	title = {A proposal of Evolutionary Prototype Selection for Class Imbalance Problems},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)},
	series = {LNCS},
	volume = {4224},
	year = {2006},
	pages = {1415-1423},
	author = {S. Garc{\'\i}a and J. R. Cano and A. Fern{\'a}ndez and F. Herrera}
}
@inbook {simidat104,
	title = {Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System},
	booktitle = {Fuzzy Applications in Industrial Engineering, Studies in Fuzziness and Soft Computing},
	volume = {201},
	year = {2006},
	pages = {89-117},
	isbn = {978-3-540-33516-0},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {Cengiz Kahraman}
}
@conference {simidat38,
	title = {Genetic Lateral and Amplitude Tuning with Rule Selection for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems},
	booktitle = {19th International Conference on Industrial \& Engineering Applications of Artificial Intelligence \& Expert Systems (IEA/AIE)},
	series = {LNCS},
	volume = {4031},
	year = {2006},
	pages = {452-461},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Annecy (France)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat39,
	title = {Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection},
	booktitle = {Modeling Decisions for Artificial Intelligence (MDAI)},
	series = {LNCS},
	volume = {3885},
	year = {2006},
	pages = {317-328},
	publisher = {Springer},
	organization = {Springer},
	address = {Tarragona (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat33,
	title = {Incorporating Knowledge in Evolutionary Prototype Selection},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)},
	series = {LNCS},
	volume = {4224},
	year = {2006},
	pages = {1358-1366},
	author = {S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
@conference {simidat36,
	title = {Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing},
	booktitle = {6th Industrial Conference on Data Mining (ICDM)},
	series = {LNCS},
	volume = {4065},
	year = {2006},
	pages = {337-349},
	publisher = {Springer},
	organization = {Springer},
	address = {Leipzig (Germany)},
	author = {F. J. Berlanga and M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@conference {simidat105,
	title = {Obtaining Compact and Still Accurate Linguistic Fuzzy Rule-Based Systems by Using Multi-Objetive Genetic Algorithms},
	booktitle = {Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS)},
	year = {2006},
	pages = {53-62},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat35,
	title = {On the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining},
	journal = {Applied Soft Computing},
	volume = {6},
	year = {2006},
	pages = {323-332},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {simidat71,
	title = {Un primer estudio sobre el uso de los sistemas de clasificaci{\'o}n basados en reglas difusas en problemas de clasificaci{\'o}n con clases no balanceadas},
	booktitle = {Proceedings of the XIII Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2006},
	month = {September},
	pages = {89-95},
	address = {Ciudad Real (Spain)},
	author = {A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera and M. J. del Jesus}
}
@conference {article,
	title = {Un primer estudio sobre el uso de los sistemas de clasificaci{\'o}n basados en reglas difusas en problemas de clasificaci{\'o}n con clases no balanceadas},
	booktitle = {XIV Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy},
	year = {2006},
	month = {01},
	address = {Ciudad Real (Espa{\~n}ol)},
	author = {Fern{\'a}ndez, Alberto and Garc{\'\i}a, Salvador and F. Herrera and M. J. del Jesus}
}
@inbook {simidat26,
	title = {A Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining},
	booktitle = {Soft Computing: Methodologies and Applications},
	year = {2005},
	pages = {271-284},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {J. R. Cano and F. Herrera and M. Lozano},
	editor = {F. Hoffmann and M. K{\"o}ppen and F. Klawonn and R. Roy}
}
@conference {simidat102,
	title = {Ajuste Evolutivo Lateral y de Amplitud de etiquetas para Sistemas Basados en Reglas Difusas},
	booktitle = {Simposio de Inteligencia Computacional (SICO)},
	year = {2005},
	pages = {481-488},
	address = {Granada (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat30,
	title = {Aprendizaje de reglas difusas mediante programaci{\'o}n gen{\'e}tica en problemas con alta dimensionalidad},
	booktitle = {I Simposio sobre L{\'o}gica Fuzzy y Soft Computing (LFSC)},
	year = {2005},
	pages = {93-100},
	address = {Granada (Spain)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@inbook {simidat22,
	title = {Evolutionary Induction of Descriptive Rules in a Market Problem},
	booktitle = {Intelligent Data Mining. Techniques and Applications, Studies in Computational Intelligence},
	volume = {5},
	year = {2005},
	pages = {267-292},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero},
	editor = {D. Ruan and G. Chen and E.E. Kerre and G. Wets}
}
@conference {simidat103,
	title = {Genetic Lateral and Amplitude Tuning of Membership Functions for Fuzzy Systems},
	booktitle = {Proceedings of the 2nd International Conference on Machine Intelligence (ACIDCA-ICMI)},
	year = {2005},
	pages = {589-595},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat23,
	title = {Genetic tuning of fuzzy rule deep structures preserving interpretability for linguistic modeling},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {13},
	number = {1},
	year = {2005},
	pages = {13-29},
	author = {J. Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {simidat20,
	title = {Inducci{\'o}n evolutiva multiobjetivo de reglas de descripci{\'o}n de subgrupos en un problema de marketing},
	booktitle = {IV Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2005},
	pages = {661-669},
	address = {Granada (Spain)},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera}
}
@inbook {Cano2005,
	title = {Instance Selection Using Evolutionary Algorithms: An Experimental Study},
	booktitle = {Advanced Techniques in Knowledge Discovery and Data Mining},
	year = {2005},
	pages = {127{\textendash}152},
	publisher = {Springer London},
	organization = {Springer London},
	address = {London},
	abstract = {In this chapter, we carry out an empirical study of the performance of four representative evolutionary algorithm models considering two instance-selection perspectives, the prototype selection and the training set selection for data reduction in knowledge discovery. This study includes a comparison between these algorithms and other nonevolutionary instance-selection algorithms. The results show that the evolutionary instance-selection algorithms consistently outperform the nonevolutionary ones, offering two main advantages simultaneously, better instance-reduction rates and higher classification accuracy.},
	isbn = {978-1-84628-183-9},
	doi = {10.1007/1-84628-183-0_5},
	url = {https://doi.org/10.1007/1-84628-183-0_5},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel},
	editor = {Pal, Nikhil R. and Jain, Lakhmi}
}
@conference {simidat29,
	title = {Learning compact fuzzy rule-based classification systems with genetic programming},
	booktitle = {4th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT)},
	year = {2005},
	pages = {1027-1032},
	address = {Barcelona (Spain)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@conference {simidat28,
	title = {Learning fuzzy rules using genetic programming: Context-free grammar definition for high-dimensionality problems},
	booktitle = {I International Workshop on Genetic Fuzzy Systems (GFS)},
	year = {2005},
	pages = {136-141},
	address = {Granada (Spain)},
	author = {F. J. Berlanga and M. J. del Jesus and F. Herrera}
}
@conference {simidat31,
	title = {Multiobjective evolutionary induction of subgroup discovery rules in a market problem},
	booktitle = {2nd International Conference on Machine Intelligence (ACIDCA-ICMI)},
	year = {2005},
	pages = {610-617},
	address = {Tozeur (Tunisia)},
	author = {F. J. Berlanga and M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera}
}
@inbook {inbook,
	title = {Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms},
	year = {2005},
	month = {01},
	pages = {85-96},
	doi = {10.1007/3-540-32400-3_7},
	author = {Lozano, Manuel and F. Herrera and J. R. Cano}
}
@inbook {Cano2005,
	title = {Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining},
	booktitle = {Evolutionary Computation in Data Mining},
	year = {2005},
	pages = {21{\textendash}39},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As instance selection can be viewed as a search problem, it could be solved using evolutionary algorithms.},
	isbn = {978-3-540-32358-7},
	doi = {10.1007/3-540-32358-9_2},
	url = {https://doi.org/10.1007/3-540-32358-9_2},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel},
	editor = {Ghosh, Ashish and Jain, Lakhmi C.}
}
@article {simidat24,
	title = {Stratification for Scaling Up Evolutionary Prototype Selection},
	journal = {Pattern Recognition Letters},
	volume = {26},
	year = {2005},
	pages = {953-963},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@inbook {691,
	title = {T{\'e}cnicas de reducci{\'o}n de datos en KDD},
	booktitle = {Miner{\'\i}a de datos: T{\'e}cnicas y Aplicaciones},
	number = {13-33},
	year = {2005},
	publisher = {Herrera F., Riguelme J.C. y Aguilar-Ruiz, JS},
	organization = {Herrera F., Riguelme J.C. y Aguilar-Ruiz, JS},
	address = {Sevilla (Espa{\~n}a)},
	issn = {84-921873-7-9},
	author = {J. R. Cano and F. Herrera}
}
@conference {simidat12,
	title = {Algoritmo Evolutivo de Extracci{\'o}n de reglas de Asociaci{\'o}n aplicado a un problema de marketing},
	booktitle = {III Congreso Espa{\~n}ol de Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados(MAEB)},
	year = {2004},
	pages = {102-104},
	address = {C{\'o}rdoba (Spain)},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@inbook {696,
	title = {Extacci{\'o}n de conocimiento con algoritmos evolutivos y reglas difusas},
	booktitle = {Introducci{\'o}n a la miner{\'\i}a de datos},
	year = {2004},
	pages = {383-420},
	publisher = {Pearson Prentice Hall},
	organization = {Pearson Prentice Hall},
	issn = {84-205-4091-9},
	author = {del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and P. Gonz{\'a}lez and F. Herrera}
}
@conference {simidat13,
	title = {Extracci{\'o}n de reglas DNF difusas en un problema de marketing},
	booktitle = {XII Congreso Espa{\~n}ol de Tecnolog{\'\i}as y L{\'o}gica Difusa(ESTYLF)},
	year = {2004},
	pages = {351-356},
	address = {Ja{\'e}n (Spain)},
	author = {M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Mesonero}
}
@inbook {inbook,
	title = {Proyecto KEEL: Desarrollo de una herramienta para el an{\'a}lisis e implementaci{\'o}n de algoritmos de extracci{\'o}n de conocimiento evolutivos},
	year = {2004},
	month = {01},
	pages = {413-424},
	isbn = {84-688-8442-1},
	author = {Alcala-Fdez, Jesus and M. J. del Jesus and Garrell, Josep-Maria and F. Herrera and Mart{\'\i}nez, Cesar and S{\'a}nchez, Luciano}
}
@inbook {simidat15,
	title = {Selecci{\'o}n Evolutiva Estratificada de Conjuntos de Entrenamiento para la Obtenci{\'o}n de Bases de Reglas con un Alto Equilibrio entre Precisi{\'o}n e Interpretabilidad},
	booktitle = {Tendencias de la Miner{\'\i}a de Datos en Spain.},
	year = {2004},
	pages = {263 - 274},
	isbn = {84-688-8442-1},
	author = {J. R. Cano and F. Herrera and M. Lozano},
	editor = {R. Gir{\'a}ldez and J. C. Riquelme and J. S. Aguilar}
}
@inbook {697,
	title = {Un estudio emp{\'\i}rico preliminar sobre los tests estad{\'\i}sticos m{\'a}s habituales en el aprendizaje autom{\'a}tico},
	booktitle = {Tendencias de la miner{\'\i}a de datos en Espa{\~n}a},
	number = {403-412},
	year = {2004},
	publisher = {R. Gir{\'a}ldez, J.C. Riquelme, J.S. Aguilar},
	organization = {R. Gir{\'a}ldez, J.C. Riquelme, J.S. Aguilar},
	address = {Santander (Espa{\~n}a)},
	issn = {84-688-8442-1},
	author = {F. Herrera and Hervas-Mart{\'\i}nez, Cesar and Otero, J and S{\'a}nchez, Luciano}
}
@inbook {CORDON2003315,
	title = {- A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems},
	booktitle = {Intelligent Systems for Information Processing},
	year = {2003},
	pages = {315 - 326},
	publisher = {Elsevier Science},
	organization = {Elsevier Science},
	address = {Amsterdam},
	abstract = {Publisher Summary This chapter illustrates a multiobjective genetic algorithm for feature selection and database learning in Fuzzy Rule-Based Classification System (FRBCS). An FRBCS presents two main components{\textemdash}the Inference System and the Knowledge Base (KB). The KB is composed of the Rule Base (RB) constituted by the collection of fuzzy rules, and of the Data Base (DB), containing the membership functions of the fuzzy partitions associated to the linguistic variables. The composition of the KB of an FRBCS directly depends on the problem being solved. If there is no expert information about the problem under solving, an automatic learning process must be used to derive the KB from examples. This contribution proposes a multiobjective genetic process for jointly performing feature selection and DB components learning that is combined with an efficient fuzzy classification rule generation method to obtain the complete KB for a descriptive FRBCS. This method achieves an important reduction of the relevant variables selected for the final system and adapts the fuzzy partition of each variable to the problem being solved. Therefore, the conclusion is that the proposed method allows for enhancing interpretability, accuracy, and performance of the FRBCS method.},
	isbn = {978-0-444-51379-3},
	doi = {https://doi.org/10.1016/B978-044451379-3/50026-1},
	url = {http://www.sciencedirect.com/science/article/pii/B9780444513793500261},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and L. Magdalena and A.M. S{\'a}nchez and P. Villar},
	editor = {Bernadette Bouchon-Meunier and Laurent Foulloy and Ronald R. Yager}
}
@inbook {Cord{\'o}n2003,
	title = {A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems},
	booktitle = {Interpretability Issues in Fuzzy Modeling},
	year = {2003},
	pages = {79{\textendash}99},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy Rule-Based Classification System (FRBCS) and to automatically learn the whole Data Base definition using a non linear scaling function to adapt the fuzzy partition contexts and determining an appropiate granularity for each of them. An ad-hoc data covering learning method is considered to obtain the Rule Base. The method uses a multiobjective genetic algorithm in order to obtain a good trade-off between accuracy and interpretability.},
	isbn = {978-3-540-37057-4},
	doi = {10.1007/978-3-540-37057-4_4},
	url = {https://doi.org/10.1007/978-3-540-37057-4_4},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Magdalena, Luis and Villar, Pedro},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@conference {simidat7,
	title = {An Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining},
	booktitle = {Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications},
	year = {2003},
	month = {September},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {simidat8,
	title = {Extracci{\'o}n Evolutiva de Reglas de Asociaci{\'o}n en un Servicio de Urgencias Psiqui{\'a}tricas},
	booktitle = {II Congreso espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos evolutivos y bioinspirados(MAEB)},
	year = {2003},
	pages = {548-555},
	address = {Gij{\'o}n(Spain)},
	author = {J. Aguilera and M. J. del Jesus and P. Gonz{\'a}lez and F. Herrera and M. Nav{\'\i}o and J. S{\'a}inz}
}
@article {simidat5,
	title = {Linguistic Modeling with Hierarchical Systems of Weighted Linguistic Rules},
	journal = {International Journal of Approximate Reasoning},
	volume = {32},
	number = {2-3},
	year = {2003},
	pages = {187-215},
	author = {R. Alcal{\'a} and J. R. Cano and O. Cord{\'o}n and F. Herrera and P. Villar and I. Zwir}
}
@conference {simidat6,
	title = {Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms},
	booktitle = {Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications},
	year = {2003},
	month = {September},
	author = {M. Lozano and F. Herrera and J. R. Cano}
}
@inbook {S{\'a}nchez2003,
	title = {Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?},
	booktitle = {Accuracy Improvements in Linguistic Fuzzy Modeling},
	year = {2003},
	pages = {366{\textendash}385},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {The accuracy of linguistic classifiers can be improved with several techniques, but they all compromise the interpretability of the rule base up to a certain degree. Assigning weights to fuzzy rules and tuning the memberships associated to linguistic variables are two of the most common methods. In this work we study whether tuning the membership functions in a linguistic classifier is better or not than adjusting rule weights, in terms of the interpretability of the rule base and the complexity of the output.},
	isbn = {978-3-540-37058-1},
	doi = {10.1007/978-3-540-37058-1_15},
	url = {https://doi.org/10.1007/978-3-540-37058-1_15},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e}},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@article {simidat4,
	title = {Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: an Experimental Study},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {7},
	number = {6},
	year = {2003},
	pages = {561-575},
	author = {J. R. Cano and F. Herrera and M. Lozano}
}
@conference {CanoCHS02,
	title = {A GRASP Algorithm for Clustering},
	booktitle = {Proceedings of the 8th Ibero-American Conference on Artifical Intelligence, Seville, Spain, November 12-15, 2002,},
	year = {2002},
	pages = {214{\textendash}223},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@article {CanoCHS02,
	title = {A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure},
	journal = {Journal of Intelligent and Fuzzy Systems},
	volume = {12},
	number = {3-4},
	year = {2002},
	pages = {235{\textendash}242},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@conference {740,
	title = {Modelos Evolutivos de Extracci{\'o}n de Conocimiento en Aplicaciones M{\'e}dicas: Enfermedad de Parkinson y Urgencias PSiqui{\'a}tricas},
	booktitle = {Workshop de Miner{\'\i}a de Datos y Aprendizaje Autom{\'a}tico},
	year = {2002},
	month = {01},
	address = {Santander (Espa{\~n}a)},
	author = {Jos{\'e} Aguilera and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and P. Gonz{\'a}lez and F. Herrera and Nav{\'\i}o, M. and Saiz, J.}
}
@conference {739,
	title = {Selecci{\'o}n Evolutiva de Instancias en Miner{\'\i}a de Datos},
	booktitle = {Workwhop de Miner{\'\i}a de Datos y Aprendizaje Autom{\'a}tico},
	year = {2002},
	month = {01},
	address = {Santander (Espa{\~n}a)},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel}
}
@conference {741,
	title = {Utilizaci{\'o}n de Algoritmos Gen{\'e}ticos Multiobjetivos para la Selecci{\'o}n de Caracter{\'\i}sticas y Dise{\~n}o de la Base de Conocimiento de un Sistema de Clasificaci{\'o}n Basado en Reglas Difusas},
	booktitle = {Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy},
	year = {2002},
	month = {01},
	address = {Le{\'o}n (Espa{\~n}a)},
	author = {O. Cord{\'o}n and F. Herrera and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and Magdalena, Luis and A.M. S{\'a}nchez and Villar, Pedro}
}
@conference {inproceedings,
	title = {A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems},
	volume = {3},
	year = {2001},
	month = {08},
	pages = {1253 - 1258 vol.3},
	isbn = {0-7803-7078-3},
	doi = {10.1109/NAFIPS.2001.943727},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and Villar, P}
}
@conference {10.1007/3-540-45497-7_29,
	title = {Feature Selection Algorithms Applied to Parkinson{\textquoteright}s Disease},
	booktitle = {Medical Data Analysis},
	year = {2001},
	pages = {195{\textendash}200},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In Parkinson{\textquoteright}s Disease an analysis of Medical Data could highlight some symptoms, which can be used as a complementary tool in an early diagnosis. This paper analyses some Filter and Wrapper Feature Selection Algorithms and combinations of them that determine some relevant features in relation to this problem. The experimentation carried out with a data set of patients allows us to determine a set of different premorbid personality traits that can be considered in the early diagnosis of Parkinsonism.},
	isbn = {978-3-540-45497-7},
	author = {Nav{\'\i}o, M. and Jos{\'e} Aguilera and M. J. del Jesus and Gonz{\'a}lez, R. and F. Herrera and Ir{\'\i}bar, C.},
	editor = {Crespo, Jose and Maojo, Victor and Martin, Fernando}
}
@article {simidat161,
	title = {Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process},
	journal = {Information Sciences},
	volume = {136},
	year = {2001},
	pages = {135-157},
	author = {J. Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {CASILLAS2001135,
	title = {Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems},
	journal = {Information Sciences},
	volume = {136},
	number = {1},
	year = {2001},
	note = {Recent Advances in Genetic Fuzzy Systems},
	pages = {135 - 157},
	abstract = {The inductive learning of a fuzzy rule-based classification system (FRBCS) is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high-classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods.},
	keywords = {feature selection, Fuzzy reasoning methods, fuzzy rule-based classification systems, Inductive learning},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/S0020-0255(01)00147-5},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025501001475},
	author = {J Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {943783,
	title = {Genetic tuning of fuzzy rule-based systems integrating linguistic hedges},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1570-1574 vol.3},
	abstract = {Tuning fuzzy rule-based systems for linguistic modeling is an interesting and widely developed task. It involves adjusting the membership functions composing the knowledge base. To do that, changing the parameters defining each membership function as using linguistic hedges to slightly modify them may be considered. This paper introduces a genetic tuning process for jointly making these two tuning approaches. The experimental results show that our method obtains accurate linguistic models in both approximation and generalization aspects.},
	keywords = {Computer science, experimental results, fuzzy logic, Fuzzy rule-based systems, Fuzzy sets, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, genetic tuning process, knowledge base, knowledge based systems, linguistic hedges, linguistic modeling, membership functions, Proposals, Shape, Takagi-Sugeno model, Timing, uncertainty handling},
	doi = {10.1109/NAFIPS.2001.943783},
	author = {J. Casillas and O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
@conference {735,
	title = {Selecting Fuzzy-Ruled Based Classification System with Specific Reasoning Methods Using Genetical Algorithms},
	booktitle = {Joint 9th IFSA World congres and 20th Nafips International},
	year = {2001},
	month = {07},
	address = {Praga},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@inbook {Cord{\'o}n2000,
	title = {Different Proposals to Improve the Accuracy of Fuzzy Linguistic Modeling},
	booktitle = {Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications},
	year = {2000},
	pages = {189{\textendash}221},
	publisher = {Springer US},
	organization = {Springer US},
	address = {Boston, MA},
	abstract = {Nowadays, Linguistic Modeling is considered as one of the most important applications of Fuzzy Set Theory, along with Fuzzy Control. Linguistic models have the advantage of providing a human-readable description of the system modeled in the form of a set of linguistic rules. In this contribution, we will analyze several approaches to improve the accuracy of linguistic models while maintaining their descriptive power. All these approaches will share the common idea of improving the way in which the Fuzzy Rule-Based System performs interpolative reasoning by improving the cooperation between the rules in the linguistic model Knowledge Base.},
	isbn = {978-1-4615-4513-2},
	doi = {10.1007/978-1-4615-4513-2_9},
	url = {https://doi.org/10.1007/978-1-4615-4513-2_9},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and Villar, Pedro and Zwir, Igor},
	editor = {Ruan, Da and Kerre, Etienne E.}
}
@conference {728,
	title = {Influencia del uso de modificadores ling{\"u}{\'\i}sticos y grados de certeza en un sistema de clasificaci{\'o}n basados en reglas difusas},
	booktitle = {X Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y reglas difusas},
	year = {2000},
	month = {09},
	address = {Santander (Espa{\~n}a)},
	author = {M. J. del Jesus and F. Herrera}
}
@article {simidat162,
	title = {A proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems},
	journal = {International Journal of Approximate Reasoning},
	volume = {20},
	year = {1999},
	pages = {21-45},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {simidat164,
	title = {Analyzing the Reasoning Mechanisms in Fuzzy Rule-Based Classification Systems},
	journal = {Mathware \& Soft Computing},
	volume = {5},
	year = {1999},
	pages = {321-332},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {Cordn1999EvolutionaryAT,
	title = {Evolutionary approaches to the learning of fuzzy rule-based classification systems},
	year = {1999},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
@conference {716,
	title = {Hibridaci{\'o}n de m{\'e}todos filtro y de envoltura para selecci{\'o}n de caracter{\'\i}sticas},
	booktitle = {Conferencia de la asociaci{\'o}n Espa{\~n}ola para la inteligencia artificial. III Jornada de transferencia tecnol{\'o}gica de inteligencia artificial.},
	year = {1999},
	month = {01},
	address = {Murcia (Espa{\~n}a)},
	author = {Aguilera Garc{\'\i}a, Jose Joaquin and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@article {simidat163,
	title = {MOGUL: A Methodology to Obtain Genetic fuzzy rule-based systems Ander the iterative rule Learning Approach},
	journal = {International Journal of Intelligent Systems},
	volume = {14},
	year = {1999},
	pages = {1123-1153},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {Cord{\'o}n1999,
	title = {Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques},
	journal = {Applied Intelligence},
	volume = {10},
	number = {1},
	year = {1999},
	month = {Jan},
	pages = {5{\textendash}24},
	abstract = {Real-world electrical engineering problems can take advantage of the last Data Analysis methodologies. In this paper we will show that Genetic Fuzzy Rule-Based Systems and Genetic Programming techniques are good choices for tackling with some practical modeling problems. We claim that both evolutionary processes may produce good numerical results while providing us with a model that can be interpreted by a human being. We will analyze in detail the characteristics of these two methods and we will compare them to the some of the most popular classical statistical modeling methods and neural networks.},
	issn = {1573-7497},
	doi = {10.1023/A:1008384630089},
	url = {https://doi.org/10.1023/A:1008384630089},
	author = {O. Cord{\'o}n and F. Herrera and S{\'a}nchez, Luciano}
}
@article {758,
	title = {Analyzing the Reasoning Mechanism in Fuzzy Rule-Based Classification Systems},
	journal = {Mathware \& Soft Computing},
	volume = {5},
	year = {1998},
	pages = {321-332},
	issn = {1134-5632},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/3-540-64582-9_778,
	title = {Computing the spanish medium electrical line maintenance costs by means of evolution-based learning processes},
	booktitle = {Methodology and Tools in Knowledge-Based Systems},
	year = {1998},
	pages = {478{\textendash}486},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we deal with the problem of computing the maintenance costs of electrical medium line in spanish towns. To do so, we present two Data Analysis tools taking as a base Evolutionary Algorithms, the Interval Genetic Algorithm-Programming method to perform symbolic regression and Genetic Fuzzy Rule-Based Systems to design fuzzy models, and use them to solve the said problem. Results obtained are compared with other kind of techniques: classical regression and neural modeling.},
	isbn = {978-3-540-69348-2},
	author = {O. Cord{\'o}n and F. Herrera and S{\'a}nchez, Luciano},
	editor = {Mira, Jos{\'e} and del Pobil, Angel Pasqual and Ali, Moonis}
}
@conference {711,
	title = {Estimaci{\'o}n de la Longitud de L{\'\i}nea de Baja Tensi{\'o}n Mediante T{\'e}cnicas Evolutivas de An{\'a}lisis de Datos},
	booktitle = {8{\textordfeminine} Reuni{\'o}n Nacional de Grupos de Investigaci{\'o}n en Ingenier{\'\i}a El{\'e}ctrica},
	year = {1998},
	author = {O. Cord{\'o}n and Sp{\'\i}n, Antonio and Fajardo, Waldo and F. Herrera and S{\'a}nchez, Luciano}
}
@article {simidat165,
	title = {Genetic Learning of Fuzzy Rule-Based Classification Systems Cooperating with Fuzzy Reasoning Methods},
	journal = {International Journal of Intelligent Systems},
	volume = {13},
	number = {10/11},
	year = {1998},
	pages = {1025-1053},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {713,
	title = {M{\'e}todos de razonamiento aproximado basados en el concepto de mayor{\'\i}a difusa para sistemas de clasificaci{\'o}n},
	booktitle = {Congreso espa{\~n}ol sobre tecnolog{\'\i}a y l{\'o}gica fuzzy},
	year = {1998},
	month = {09},
	address = {Pamplona (Espa{\~n}a)},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@article {simidat166,
	title = {Modelado cualitativo utilizando una metodolog{\'\i}a evolutiva de aprendizaje iterativo de bases de reglas difusas},
	journal = {Revista Iberoamericana de Inteligencia Artificial},
	volume = {50},
	year = {1998},
	pages = {56-61},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and M. Lozano}
}
@conference {inproceedings,
	title = {An evolutionary paradigm for designing fuzzy rule-based systems from examples},
	year = {1997},
	month = {10},
	pages = {139 - 144},
	isbn = {0-85296-693-8},
	doi = {10.1049/cp:19971170},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Lozano, Manuel}
}
@conference {708,
	title = {Nuevos m{\'e}todos de razonamiento en sistemas de clasificaci{\'o}n basados en reglas difusas},
	booktitle = {Congreso espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy.},
	year = {1997},
	address = {Tarragona},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@conference {707,
	title = {Sistema de Clasificaci{\'o}n con Reglas Difusas Utilizando Algoritmos Gen{\'e}ticos},
	booktitle = {VI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF{\textquoteright}96)},
	year = {1996},
	address = {Oviedo},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
