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Feature Selection in Genetic Fuzzy Discretization for the Pattern Classification Problems Yoon-Seok CHOI Byung-Ro MOON Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.7 pp.1047-1054 Publication Date: 2007/07/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.7.1047 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Pattern Recognition Keyword: genetic algorithm, fuzzy discretization, feature selection, pattern classification, Full Text: PDF(275.6KB)>>
Summary: We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized and a discretization process increases the total amount of data being transformed. We use a genetic algorithm with feature selection not only to optimize these parameters but also to reduce the amount of transformed data by filtering the unconcerned attributes. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization with feature selection. | ||||||||||
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