MINING SIMPLIFIED FUZZY IF-THEN RULES FOR PATTERN CLASSIFICATION

Yi-Chung Hu () and Fang-Mei Tseng
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Yi-Chung Hu: Department of Business Administration, Chung Yuan Christian University, Chung-Li, Taiwan, ROC
Fang-Mei Tseng: Department of International Business, Yuan Ze University, Chung-Li, Taiwan, ROC

International Journal of Information Technology & Decision Making (IJITDM), 2009, vol. 08, issue 03, 473-489

Abstract: A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Simplified fuzzy if-then rules have been widely used in function approximation problems due to no complicated defuzzification is required. The proposed simplified fuzzy rule-based classification system, whose number of output is equal to the number of different classes, approximates an unknown mapping from input to desired output for each discriminant function. Not only a fuzzy data mining method is proposed to find simplified fuzzy if-then rules from training data, but also the genetic algorithm is employed to determine some user-specified parameters. To evaluate the classification performance of the proposed method, computer simulations are performed on some well-known datasets, showing that the generalization ability of the proposed method is comparable to the other fuzzy or nonfuzzy methods.

Keywords: Data mining; fuzzy sets; simplified fuzzy rules; genetic algorithm; discriminant function (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S021962200900348X

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