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hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems |
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| Authors: | Emel K?z?lkaya AydoganIsmail Karaoglan Panos M Pardalos |
| Affiliation: | a Department of Industrial Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey b Department of Industrial Engineering, Faculty of Engineering, Selcuk University, Konya, Turkey c Department of Industrial and System Engineering, Faculty of Engineering, University of Florida, Gainesville, United States d Laboratory of Algorithms and Technologies for Networks Analysis (LATNA), National Research University Higher School of Economics 20, Myasnitskaya st. Moscow, 101000, Russia |
| 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. |
| Keywords: | Fuzzy rule based classification systems Genetic algorithms Genetic fuzzy systems Classification Integer programming |
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