Pattern classification on the basis of unnecessary attributes reduction in fuzzy rule-based systems


Chang-Sik Son, Doo-Ywan Kim, Journal of Information Processing Systems Vol. 8, No. 3, pp. 109-118, Jun. 2007  


Keywords: Fuzzy Pattern Classification, Attribution Reduction, Rough Set
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Abstract

This paper proposed a method that can be simply analyzed instead of the basic general Fuzzy rule that its insufficient characters are cut out. Based on the proposed method. Rough sets are used to eliminate the incomplete attributes included in the rule and also for a classification more precise; the agreement of the membership function's output extracted the maximum attributes. Besides, the proposed method in the simulation shows that in order to verify the validity, compare the max-product result of fuzzy before and after reducing rule hosed on the rice taste data; then, we can see that both the max-product result of fuzzy before and after reducing rule are exactly the same; for a verification more objective, we compared the defuzzificated real number section.


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Cite this article
[APA Style]
Son, C. & Kim, D. (2007). Pattern classification on the basis of unnecessary attributes reduction in fuzzy rule-based systems. Journal of Internet Computing and Services, 8(3), 109-118.

[IEEE Style]
C. Son and D. Kim, "Pattern classification on the basis of unnecessary attributes reduction in fuzzy rule-based systems," Journal of Internet Computing and Services, vol. 8, no. 3, pp. 109-118, 2007.

[ACM Style]
Chang-Sik Son and Doo-Ywan Kim. 2007. Pattern classification on the basis of unnecessary attributes reduction in fuzzy rule-based systems. Journal of Internet Computing and Services, 8, 3, (2007), 109-118.