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Mining Simplified Fuzzy If-Then Rules For Pattern Classification

Author

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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)

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.

Suggested Citation

  • Yi-Chung Hu & Fang-Mei Tseng, 2009. "Mining Simplified Fuzzy If-Then Rules For Pattern Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 473-489.
    • Handle: RePEc:wsi:ijitdm:v:08:y:2009:i:03:n:s021962200900348x
    DOI: 10.1142/S021962200900348X
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    Cited by:

    1. Si He & Nabil Belacel & Alan Chan & Habib Hamam & Yassine Bouslimani, 2016. "A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 949-974, September.
    2. Chun-Hao Chen & Tzung-Pei Hong & Yeong-Chyi Lee & Vincent S. Tseng, 2015. "Finding Active Membership Functions for Genetic-Fuzzy Data Mining," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1215-1242, November.

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