GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems

TitleGP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems
Publication TypeJournal Article
Year of Publication2010
AuthorsBerlanga, F.J., Rivera-Rivas A.J., del Jesus M. J., and Herrera F.
JournalInformation Sciences
Volume180
Number8
Pagination1183 - 1200
ISSN0020-0255
Keywordsclassification, Fuzzy rule-based systems, Genetic Fuzzy Systems, Genetic programming, High-dimensional problems, Interpretability-accuracy trade-off
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.

URLhttp://www.sciencedirect.com/science/article/pii/S0020025509005635
DOI10.1016/j.ins.2009.12.020