Title | GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Berlanga, F.J., Rivera-Rivas A.J., del Jesus M. J., and Herrera F. |
Journal | Information Sciences |
Volume | 180 |
Number | 8 |
Pagination | 1183 - 1200 |
ISSN | 0020-0255 |
Keywords | classification, 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. |
URL | http://www.sciencedirect.com/science/article/pii/S0020025509005635 |
DOI | 10.1016/j.ins.2009.12.020 |