hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems
APPLIED SOFT COMPUTING, cilt.12, ss.800-806, 2012 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 12
- Basım Tarihi: 2012
- Doi Numarası: 10.1016/j.asoc.2011.10.010
- Dergi Adı: APPLIED SOFT COMPUTING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.800-806
- Anahtar Kelimeler: Fuzzy rule based classification systems, Genetic algorithms, Genetic fuzzy systems, Classification, Integer programming
- Erciyes Üniversitesi Adresli: Hayır
Özet
The aim of this work is to propose a hybrid heuristic approach (called hGA) based on genetic algorithm (GA) and integer-programming formulation (IPF) to solve high dimensional classification problems in linguistic fuzzy rule-based classification systems. In this algorithm, each chromosome represents a rule for specified class, GA is used for producing several rules for each class, and finally IPF is used for selection of rules from a pool of rules, which are obtained by GA. The proposed algorithm is experimentally evaluated by the use of non-parametric statistical tests on seventeen classification benchmark data sets. Results of the comparative study show that hGA is able to discover accurate and concise classification rules. Published by Elsevier B.V.