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

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{Berlanga20101183,

• author = "F. J. Berlanga and A. J. Rivera and M. J. {del Jesus} and F. Herrera",
• title = "GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems",
• journal = "Information Sciences",
• volume = "180",
• number = "8",
• pages = "1183--1200",
• year = "2010",
• ISSN = "0020-0255",
• DOI = "doi:10.1016/j.ins.2009.12.020",
• URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34R0J-1/2/82039ab1549f5a0d0fc4d73b2a30bfa6",
• keywords = "genetic algorithms, genetic programming, Classification, Fuzzy rule-based systems, Genetic fuzzy systems, 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.",

}

Genetic Programming entries for Francisco Jose Berlanga Antonio Jesus Rivera Rivas Maria Jose del Jesus Francisco Herrera

Citations