@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 {CANO2019,
	title = {Label noise filtering techniques to improve monotonic classification},
	journal = {Neurocomputing},
	volume = {353},
	year = {2019},
	note = {TIN2014-57251-P; TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS},
	month = {08/2019},
	pages = {83-95},
	abstract = {The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are very frequent. To construct predictive monotone models from those problems, many classifiers require as input a data set satisfying the monotonicity relationships among all samples. Changing the class labels of the data set (relabeling) is useful for this. Relabeling is assumed to be an important building block for the construction of monotone classifiers and it is proved that it can improve the predictive performance. In this paper, we will address the construction of monotone datasets considering as noise the cases that do not meet the monotonicity restrictions. For the first time in the specialized literature, we propose the use of noise filtering algorithms in a preprocessing stage with a double goal: to increase both the monotonicity index of the models and the accuracy of the predictions for different monotonic classifiers. The experiments are performed over 12 datasets coming from classification and regression problems and show that our scheme improves the prediction capabilities of the monotonic classifiers instead of being applied to original and relabeled datasets. In addition, we have included the analysis of noise filtering process in the particular case of wine quality classification to understand its effect in the predictive models generated.},
	keywords = {Monotonic classification, Noise filtering, Ordinal classification, Preprocessing},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2018.05.131},
	url = {http://www.sciencedirect.com/science/article/pii/S092523121930325X},
	author = {J. R. Cano and J. Luengo and S. Garc{\'\i}a}
}
@conference {10.1007/978-3-319-92639-1_23,
	title = {A First Attempt on Monotonic Training Set Selection},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2018},
	pages = {277{\textendash}288},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Monotonicity constraints frequently appear in real-life problems. Many of the monotonic classifiers used in these cases require that the input data satisfy the monotonicity restrictions. This contribution proposes the use of training set selection to choose the most representative instances which improves the monotonic classifiers performance, fulfilling the monotonic constraints. We have developed an experiment on 30 data sets in order to demonstrate the benefits of our proposal.},
	isbn = {978-3-319-92639-1},
	author = {J. R. Cano and S. Garc{\'\i}a},
	editor = {de Cos Juez, Francisco Javier and Villar, Jos{\'e} Ram{\'o}n and de la Cal, Enrique A. and Herrero, {\'A}lvaro and Quinti{\'a}n, H{\'e}ctor and S{\'a}ez, Jos{\'e} Ant{\'o}nio and Corchado, Emilio}
}
@conference {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 {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 {Garc{\'\i}a2017,
	title = {MoNGEL: monotonic nested generalized exemplar learning},
	journal = {Pattern Analysis and Applications},
	volume = {20},
	number = {2},
	year = {2017},
	month = {May},
	pages = {441{\textendash}452},
	abstract = {In supervised prediction problems, the response attribute depends on certain explanatory attributes. Some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. In this paper, we aim at formalizing the approach to nested generalized exemplar learning with monotonicity constraints, proposing the monotonic nested generalized exemplar learning (MoNGEL) method. It accomplishes learning by storing objects in {\$}{\$}{\{}{\backslash}mathbb {\{}R{\}}{\}}^n{\$}{\$}Rn, hybridizing instance-based learning and rule learning into a combined model. An experimental analysis is carried out over a wide range of monotonic data sets. The results obtained have been verified by non-parametric statistical tests and show that MoNGEL outperforms well-known techniques for monotonic classification, such as ordinal learning model, ordinal stochastic dominance learner and k-nearest neighbor, considering accuracy, mean absolute error and simplicity of constructed models.},
	issn = {1433-755X},
	doi = {10.1007/s10044-015-0506-y},
	url = {https://doi.org/10.1007/s10044-015-0506-y},
	author = {Javier Garc{\'\i}a and Fardoun, Habib M. and Alghazzawi, Daniyal M. and J. R. Cano and S. Garc{\'\i}a}
}
@article {CANO2017128,
	title = {Prototype selection to improve monotonic nearest neighbor},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {60},
	year = {2017},
	pages = {128 - 135},
	abstract = {Student surveys occupy a central place in the evaluation of courses at teaching institutions. At the end of each course, students are requested to evaluate various aspects such as activities, methodology, coordination or resources used. In addition, a final qualification is given to summarize the quality of the course. The prediction of this final qualification can be accomplished by using monotonic classification techniques. The outcome offered by these surveys is particularly significant for faculty and teaching staff associated with the course. The monotonic nearest neighbor classifier is one of the most relevant algorithms in monotonic classification. However, it does suffer from two drawbacks, (a) inefficient execution time in classification and (b) sensitivity to no monotonic examples. Prototype selection is a data reduction process for classification based on nearest neighbor that can be used to alleviate these problems. This paper proposes a prototype selection algorithm called Monotonic Iterative Prototype Selection (MONIPS) algorithm. Our objective is two-fold. The first one is to introduce MONIPS as a method for obtaining monotonic solutions. MONIPS has proved to be competitive with classical prototype selection solutions adapted to monotonic domain. Besides, to further demonstrate the good performance of MONIPS in the context of a student survey about taught courses.},
	keywords = {Data reduction, Monotone nearest neighbor, Monotonic classification, Opinion surveys, Prototype selection},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2017.02.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197617300295},
	author = {J. R. Cano and Naif R. Aljohani and Rabeeh Ayaz Abbasi and Jalal S. Alowidbi and S. Garc{\'\i}a}
}
@article {CANO201794,
	title = {Training set selection for monotonic ordinal classification},
	journal = {Data \& Knowledge Engineering},
	volume = {112},
	year = {2017},
	pages = {94 - 105},
	abstract = {In recent years, monotonic ordinal classification has increased the focus of attention for machine learning community. Real life problems frequently have monotonicity constraints. Many of the monotonic classifiers require that the input data sets satisfy the monotonicity relationships between its samples. To address this, a conventional strategy consists of relabeling the input data to achieve complete monotonicity. As an alternative, we explore the use of preprocessing algorithms without modifying the class label of the input data. In this paper we propose the use of training set selection to choose the most effective instances which lead the monotonic classifiers to obtain more accurate and efficient models, fulfilling the monotonic constraints. To show the benefits of our proposed training set selection algorithm, called MonTSS, we carry out an experimentation over 30 data sets related to ordinal classification problems.},
	keywords = {Data preprocessing, machine learning, Monotonic classification, Ordinal classification, Training set selection},
	issn = {0169-023X},
	doi = {https://doi.org/10.1016/j.datak.2017.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0169023X16303585},
	author = {J. R. Cano and S. Garc{\'\i}a}
}
@conference {GarciaCG16,
	title = {A Nearest Hyperrectangle Monotonic Learning Method},
	booktitle = {Proceedings of the 11th International Conference Hybrid Artificial Intelligent Systems, 2016, Seville, Spain, April 18-20, 2016},
	year = {2016},
	pages = {311{\textendash}322},
	author = {Javier Garc{\'\i}a and J. R. Cano and S. Garc{\'\i}a}
}
@article {GarciaAACG16,
	title = {Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms},
	journal = {International Journal Computational Intelligence Systems},
	volume = {9},
	number = {1},
	year = {2016},
	pages = {184{\textendash}201},
	doi = {10.1080/18756891.2016.1146536},
	author = {Javier Garc{\'\i}a and Adnan Al-bar and Naif R. Aljohani and J. R. Cano and S. Garc{\'\i}a}
}
@article {Garc{{\'\i}a2015,
	title = {MoNGEL: monotonic nested generalized exemplar learning},
	journal = {Pattern Analysis and Applications},
	year = {2015},
	pages = {1{\textendash}12},
	issn = {1433-755X},
	doi = {10.1007/s10044-015-0506-y},
	author = {Javier Garc{\'\i}a and Fardoun, Habib M. and Alghazzawi, Daniyal M. and J. R. Cano and S. Garc{\'\i}a}
}
@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 {623,
	title = {Descubrimiento de subgrupos aplicado al portal de comercio electr{\'o}nico: OrOliveSur.com},
	year = {2013},
	pages = {1-16},
	institution = {Departamento de Inform{\'a}tica, Universidad de Ja{\'e}n},
	address = {Ja{\'e}n, Espa{\~n}a},
	author = {C. J. Carmona and M. J. del Jesus and S. Garc{\'\i}a}
}
@conference {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 {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 {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 {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 {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 {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}
}
@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}
}
@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}
}
@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 {simidat95,
	title = {KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems},
	journal = {Soft Computing},
	volume = {13},
	number = {3},
	year = {2009},
	pages = {307-318},
	doi = {10.1007/s00500-008-0323-y},
	author = {J. Alcal{\'a}-Fdez and L. S{\'a}nchez and S. Garc{\'\i}a and M. J. del Jesus and S. Ventura and J.M. Garrell and J. Otero and C. Romero and J. Bacardit and V. M. Rivas and J.C. Fern{\'a}ndez and F. Herrera}
}
@conference {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}
}
@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 {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}
}
@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 {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}
}
@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}
}
@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 {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 {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 {simidat34,
	title = {A proposal of Evolutionary Prototype Selection for Class Imbalance Problems},
	booktitle = {Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)},
	series = {LNCS},
	volume = {4224},
	year = {2006},
	pages = {1415-1423},
	author = {S. Garc{\'\i}a and J. R. Cano and A. Fern{\'a}ndez and F. Herrera}
}
@conference {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 {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}
}
