Automatic Generation of Fuzzy Classification Systems Using Hyper-Cone Membership Functions
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概要
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In this paper, we propose automatic generation methodsof fuzzy classification rules with the Genetic Algorithms(GAs) to obtain compact fuzzy systems. This time, wepropose an approach of hyper-cone membership functionto construct rules for the antecedent part. Then,this method is determined the location and shape ofhyper-cone membership function in the antecedent part,output class and the number of necessary inputs of eachrule by GAs. Also, using the rule addition method inGA process, compact fuzzy classification systems are obtained.Though the proposed methods are quite simple,the process of GAs on both methods presents a solvingfor two-objective optimization problems: increasing thenumbers of correct pattern classification, while decreasingthe rule and input numbers optimally. This methodwas applied to Wine data sets and Wisconsin PrognosticBreast Cancer (WPBC) data sets. Wine data sets consistof 13 inputs and three outputs, while WPBC datasets contain 33 inputs and two outputs.
- IEEEの論文
- 2003-00-00
IEEE | 論文
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