KEEL: A data mining software tool integrating genetic fuzzy systems
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María José del Jesús | Jesús Alcalá-Fdez | Francisco Herrera | Luciano Sánchez | Francisco José Berlanga | Salvador García | Alberto Fernández
[1] Stephen F. Smith,et al. Competition-based induction of decision models from examples , 1993, Machine Learning.
[2] K. De Jong,et al. Using Genetic Algorithms for Concept Learning , 2004, Machine Learning.
[3] A. E. Eiben,et al. Introduction to Evolutionary Computing , 2003, Natural Computing Series.
[4] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[5] Stewart W. Wilson. Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.
[6] Jesús S. Aguilar-Ruiz,et al. Natural Encoding for Evolutionary Supervised Learning , 2007, IEEE Transactions on Evolutionary Computation.
[7] Bernard De Baets,et al. Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study , 2007, Int. J. Approx. Reason..
[8] Kwong-Sak Leung,et al. Data Mining Using Grammar Based Genetic Programming and Applications , 2000 .
[9] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[10] D. E. Goldberg,et al. Genetic Algorithms in Search , 1989 .
[11] Ronald R. Yager,et al. Essentials of fuzzy modeling and control , 1994 .
[12] Francisco Herrera,et al. A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems , 2005, Eng. Appl. Artif. Intell..
[13] María José del Jesús,et al. MOGUL: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach , 1999, Int. J. Intell. Syst..
[14] Gilles Venturini,et al. SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts , 1993, ECML.
[15] Ester Bernadó-Mansilla,et al. Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks , 2003, Evolutionary Computation.
[16] Tim Kovacs. Strength or accuracy: credit assignment in learning classifier systems , 2003 .
[17] Paul Snow,et al. Associativity and normative credal probability , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[18] Francisco Herrera,et al. Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models , 2003, Modelling with Words.
[19] Zheru Chi,et al. Handwritten numeral recognition using self-organizing maps and fuzzy rules , 1995, Pattern Recognit..
[20] Paul C. Rhodes,et al. Essentials of Fuzzy Modelling and Control , 1995 .
[21] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[22] S. Smith,et al. A Learning System Based on Genetic Algorithms , 1980 .
[23] Francisco Herrera,et al. COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules , 2002, IEEE Trans. Syst. Man Cybern. Part B.
[24] Francisco Herrera,et al. Genetic fuzzy systems: taxonomy, current research trends and prospects , 2008, Evol. Intell..
[25] Jiann-Shing Shieh,et al. Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia. , 2006, Medical engineering & physics.
[26] Francisco Herrera,et al. Tuning fuzzy logic controllers by genetic algorithms , 1995, Int. J. Approx. Reason..
[27] Jerry M. Mendel,et al. Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..
[28] Philip R. Thrift,et al. Fuzzy Logic Synthesis with Genetic Algorithms , 1991, ICGA.
[29] Francisco Herrera,et al. Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems , 2001, Fuzzy Sets Syst..
[30] Francisco Herrera,et al. Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.
[31] Senén Barro,et al. Evolutionary learning of a fuzzy controller for wall-following behavior in mobile robotics , 2006, Soft Comput..
[32] Francisco Herrera,et al. A Two-stage Evolutionary Process for Designing Tsk Fuzzy Rule-based Systems a Two-stage Evolutionary Process for Designing Tsk Fuzzy Rule-based Systems , 1996 .
[33] Jaume Bacardit,et al. Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System , 2005, IWLCS.
[34] John H. Holland,et al. Cognitive systems based on adaptive algorithms , 1977, SGAR.
[35] Carl E. Rasmussen,et al. The Need for Open Source Software in Machine Learning , 2007, J. Mach. Learn. Res..
[36] Jesús Alcalá-Fdez,et al. Local identification of prototypes for genetic learning of accurate TSK fuzzy rule‐based systems , 2007, Int. J. Intell. Syst..
[37] Hisao Ishibuchi,et al. Rule weight specification in fuzzy rule-based classification systems , 2005, IEEE Transactions on Fuzzy Systems.
[38] Hisao Ishibuchi,et al. Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..
[39] Hisao Ishibuchi,et al. Hybridization of fuzzy GBML approaches for pattern classification problems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[40] Michael I. Jordan,et al. Learning with Mixtures of Trees , 2001, J. Mach. Learn. Res..
[41] César Hervás-Martínez,et al. JCLEC: a Java framework for evolutionary computation , 2007, Soft Comput..
[42] Antonio González Muñoz,et al. SLAVE: a genetic learning system based on an iterative approach , 1999, IEEE Trans. Fuzzy Syst..