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

共 72 条

[41] Subspace based feature selection for pattern recognition [J].

Gunal, Serkan ;

Edizkan, Rifat .

INFORMATION SCIENCES, 2008, 178 (19) :3716-3726

[42] Genetic fuzzy systems: Taxonomy, current research trends and prospects [J].

Herrera F. .

Evolutionary Intelligence, 2008, 1 (01) :27-46

[43] Incremental evolutionary design of TSK fuzzy controllers [J].

Hoffmann, Frank ;

Schauten, Daniel ;

Hoelemann, Sebastian .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2007, 15 (04) :563-577

[44]

Homaifar A., 2000, International Journal of Knowledge-Based Intelligent Engineering Systems, V4, P33

[45] Rule weight specification in fuzzy rule-based classification systems [J].

Ishibuchi, H ;

Yamamoto, T .

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2005, 13 (04) :428-435

[46] Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining [J].

Ishibuchi, H ;

Yamamoto, T .

FUZZY SETS AND SYSTEMS, 2004, 141 (01) :59-88

[47] Voting in fuzzy rule-based systems for pattern classification problems [J].

Ishibuchi, H ;

Nakashima, T ;

Morisawa, T .

FUZZY SETS AND SYSTEMS, 1999, 103 (02) :223-238

[48] Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems [J].

Ishibuchi, H ;

Nakashima, T ;

Murata, T .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1999, 29 (05) :601-618

[49]

ISHIBUCHI H, 2004, CLASSIFICATION MODEL

[50] Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning [J].

Ishibuchi, Hisao ;

Nojima, Yusuke .

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2007, 44 (01) :4-31

← 1 2 3 4 5 6 7 8 →