Selection of relevant features in a fuzzy genetic learning algorithm

Genetic algorithms offer a powerful search method for a variety of learning tasks, and there are different approaches in which they have been applied to learning processes. Structural learning algorithm on vague environment (SLAVE) is a genetic learning algorithm that uses the iterative approach to learn fuzzy rules. SLAVE can select the relevant features of the domain, but when working with large databases the search space is too large and the running time can sometimes be excessive. We propose to improve SLAVE by including a feature selection model in which the genetic algorithm works with individuals (representing individual rules) composed of two structures: one structure representing the relevance status of the involved variables in the rule, the other one representing the assignments variable/value. For this general representation, we study two alternatives depending on the information coded in the first structure. When compared with the initial algorithm, this new approach of SLAVE reduces the number of rules, simplifies the structure of the rules and improves the total accuracy.

[1]  F. Herrera,et al.  A two-stage evolutionary process for designing TSK fuzzy rule-based systems , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Antonio González Muñoz,et al.  SLAVE: a genetic learning system based on an iterative approach , 1999, IEEE Trans. Fuzzy Syst..

[3]  F. Herrera,et al.  Genetic learning of fuzzy rule‐based classification systems cooperating with fuzzy reasoning methods , 1998, Int. J. Intell. Syst..

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

[5]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[6]  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..

[7]  Luis Magdalena,et al.  Adapting the gain of an FLC with genetic algorithms , 1997, Int. J. Approx. Reason..

[8]  Luis Magdalena,et al.  A Fuzzy logic controller with learning through the evolution of its knowledge base , 1997, Int. J. Approx. Reason..

[9]  Jerzy Stefanowski,et al.  Feature subset selection for classification of histological images , 1997, Artif. Intell. Medicine.

[10]  T. Kohonen.  Self-Organizing Maps , 1995, Springer Series in Information Sciences.

[11]  Gilles Venturini,et al.  SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts , 1993, ECML.

[12]  Michael de la Maza,et al.  Book review: Genetic Algorithms + Data Structures = Evolution Programs by Zbigniew Michalewicz (Springer-Verlag, 1992) , 1993 .

[13]  Kenneth A. De Jong,et al.  Genetic algorithms as a tool for feature selection in machine learning , 1992, Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92.

[14]  R. Boggia,et al.  Genetic algorithms as a strategy for feature selection , 1992 .

[15]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[16]  Richard Lippmann,et al.  Using Genetic Algorithms to Improve Pattern Classification Performance , 1990, NIPS.

[17]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

[18]  J. E. Baker.  Adaptive Selection Methods for Genetic Algorithms , 1985, ICGA.

[19]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[20]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[21]  CsiszárImre,et al.  Information Theory and Statistics , 1959 .

[22]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

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

[24]  L. Wehenkel.  On uncertainty measures used for decision tree induction , 1996 .

[25]  Antonio González,et al.  A learning methodology in uncertain and imprecise environments , 1995 .

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

[27]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[28]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[29]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..