A new method for generating fuzzy rules from numerical data for handling classification problems
Shyi‐Ming Chen, Shao-Hua Lee, Chia-Hoang Lee · Applied Artificial Intelligence · 2001
Fuzzy classi® cation is one of the important applications of fuzzy logic.Fuzzy classi® cation Systems are capable of handling perceptual uncertainties, such as the vagueness and ambiguity involved in classi® cation problems.T he most important task to accomplish a fuzzy classi® cation system is to ® nd a set of fuzzy rules suitable for a speci® c classi® cation problem.In this article, we present a new method for generating fuzzy rules from numerical data for handling fuzzy classi® cation problems based on the fuzzy subsethood values between decisions to be made and terms of attributes by using the level threshold value ¬ and the applicability threshold value , where ¬ 2 [0; 1] and 2 [0; 1].W e apply the proposed method to deal with the ``Saturday Morning Problem,'' where the proposed method has a higher classi® cation accuracy rate and generates fewer fuzzy rules than the existing methods.Fuzzy classi® cation is one of the important applications of fuzzy logic (Zadeh, 1965(Zadeh, , 1988)).In a fuzzy classi® cation system (Yoshinari, Pedrycz, & Hirota, 1993), a case can properly be classi® ed by applying a set of fuzzy rules based on the linguistic terms (Zadeh, 1975) of its attributes.Fuzzy classi® cation systems are capable of handling perceptual uncertainties,