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

被引：100
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
收藏
页码：1183 / 1200
页数：18
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