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

被引：101

作者：

Berlanga, F. J. ^{[1
]}

Rivera, A. J. ^{[2
]}

del Jesus, M. J. ^{[2
]}

Herrera, F. ^{[3
]}

机构：

[1] Univ Zaragoza, Dept Comp Sci & Syst Engn, E-50018 Zaragoza, Spain

[2] Univ Jaen, Dept Comp Sci, E-23071 Jaen, Spain

[3] Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain

来源：

INFORMATION SCIENCES | 2010年 / 180卷 / 08期

关键词：

Classification; Genetic programming; Fuzzy rule-based systems; Genetic fuzzy systems; High-dimensional problems; Interpretability-accuracy trade-off; PATTERN-CLASSIFICATION; FEATURE-SELECTION; SPECIAL-ISSUE; INTERPRETABILITY; ALGORITHMS; MODELS; ADAPTATION; REDUCTION; SEARCH; DESIGN;

D O I：

10.1016/j.ins.2009.12.020

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2009 Elsevier Inc. All rights reserved.

引用

页码：1183 / 1200

页数：18

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