Genetic learning of fuzzy rule‐based classification systems cooperating with fuzzy reasoning methods

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule‐based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms. The results show the good behavior of the proposed method, which maintains the linguistic description of the fuzzy rules. © 1998 John Wiley & Sons, Inc.

[1]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[2]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[3]  Tsu-Tian Lee,et al.  On the design of a classifier with linguistic variables as inputs , 1983 .

[4]  L. Valverde,et al.  On Some Logical Connectives for Fuzzy Sets Theory , 1983 .

[5]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[8]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[9]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[10]  H. Ishibuchi,et al.  Distributed representation of fuzzy rules and its application to pattern classification , 1992 .

[11]  Ralph R. Martin,et al.  A Sequential Niche Technique for Multimodal Function Optimization , 1993, Evolutionary Computation.

[12]  Hisao Ishibuchi,et al.  Efficient fuzzy partition of pattern space for classification problems , 1993 .

[13]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[14]  LiMin Fu,et al.  Rule Generation from Neural Networks , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[15]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[16]  Lucien Duckstein,et al.  Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems , 1995 .

[17]  Zheru Chi,et al.  Handwritten numeral recognition using self-organizing maps and fuzzy rules , 1995, Pattern Recognit..

[18]  H. Ishibuchi,et al.  Voting schemes for fuzzy-rule-based classification systems , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[19]  Ludmila I. Kuncheva,et al.  On the Equivalence between fuzzy and Statistical Classifiers , 1996, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[20]  Hong Yan,et al.  Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition , 1996, Advances in Fuzzy Systems - Applications and Theory.

[21]  Sankar K. Pal,et al.  Fuzzy self-organization, inferencing, and rule generation , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[22]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[23]  Francisco Herrera,et al.  A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples , 1997, Int. J. Approx. Reason..

[24]  Antonio González Muñoz,et al.  Multi-stage genetic fuzzy systems based on the iterative rule learning approach , 1997 .

[25]  Shigeo Abe,et al.  A fuzzy classifier with ellipsoidal regions , 1997, IEEE Trans. Fuzzy Syst..

[26]  Francisco Herrera,et al.  A learning process for fuzzy control rules using genetic algorithms , 1998, Fuzzy Sets Syst..

[27]  Raúl Pérez,et al.  Completeness and consistency conditions for learning fuzzy rules , 1998, Fuzzy Sets Syst..

[28]  Francisco Herrera,et al.  Encouraging Cooperation in the Genetic Iterative Rule Learning Approach for Qualitative Modeling , 1999 .

[29]  F. Herrera,et al.  A proposal on reasoning methods in fuzzy rule-based classification systems , 1999 .