A NEW METHOD FOR GENERATING FUZZY RULES FROM NUMERICAL DATA FOR HANDLING CLASSIFICATION PROBLEMS

Fuzzy classification is one of the important applications of fuzzy logic. Fuzzy classification Systems are capable of handling perceptual uncertainties, such as the vagueness and ambiguity involved in classification problems. The most important task to accomplish a fuzzy classification system is to find a set of fuzzy rules suitable for a specific classification problem. In this article, we present a new method for generating fuzzy rules from numerical data for handling fuzzy classification problems based on the fuzzy subsethood values between decisions to be made and terms of attributes by using the level threshold value alpha and the applicability threshold value beta , where a (belong to)[0,1] and ? (belong to)[0,1] . We apply the proposed method to deal with the "Saturday Morning Problem," where the proposed method has a higher classification accuracy rate and generates fewer fuzzy rules than the existing methods.