Journal
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Volume 8, Issue 3, Pages 473-489Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S021962200900348X
Keywords
Data mining; fuzzy sets; simplified fuzzy rules; genetic algorithm; discriminant function
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Funding
- National Science Council of Taiwan [NSC 97-2410-H-033-015-MY2]
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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.
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