hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

Aydogan E. K., Karaoglan I., Pardalos P. M.

APPLIED SOFT COMPUTING, vol.12, pp.800-806, 2012 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12
  • Publication Date: 2012
  • Doi Number: 10.1016/j.asoc.2011.10.010
  • Journal Name: APPLIED SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.800-806
  • Keywords: Fuzzy rule based classification systems, Genetic algorithms, Genetic fuzzy systems, Classification, Integer programming
  • Erciyes University Affiliated: Yes

Abstract

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.