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

页码：1183 / 1200

页数：18

共 72 条

[41] Subspace based feature selection for pattern recognition
Gunal, Serkan
Edizkan, Rifat
[J]. INFORMATION SCIENCES, 2008, 178 (19) : 3716 - 3726
[42] Genetic fuzzy systems: Taxonomy, current research trends and prospects
Herrera F.
[J]. Evolutionary Intelligence, 2008, 1 (01) : 27 - 46
[43] Incremental evolutionary design of TSK fuzzy controllers
Hoffmann, Frank
Schauten, Daniel
Hoelemann, Sebastian
[J]. 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
Ishibuchi, H
Yamamoto, T
[J]. 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
Ishibuchi, H
Yamamoto, T
[J]. FUZZY SETS AND SYSTEMS, 2004, 141 (01) : 59 - 88
[47] Voting in fuzzy rule-based systems for pattern classification problems
Ishibuchi, H
Nakashima, T
Morisawa, T
[J]. FUZZY SETS AND SYSTEMS, 1999, 103 (02) : 223 - 238
[48] Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
Ishibuchi, H
Nakashima, T
Murata, T
[J]. 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
Ishibuchi, Hisao
Nojima, Yusuke
[J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2007, 44 (01) : 4 - 31

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