Fuzzy Classifier with Probabilistic IF-THEN Rules

Authors:
Hexin Lv;Bin Zhu;Yongchuan Tang
Affiliations:
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, Zhejiang Province, 310015, P.R. China;College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, Zhejiang Province, 310015, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, Zhejiang Province, 310027, P.R. China
Venue:
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Year:
2007

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Abstract

The typical fuzzy classifier consists of rules each one describing one of the classes. This paper presents a new fuzzy classifier with probabilistic IF-THEN rules. A learning algorithm based on the gradient descent method is proposed to identify the probabilistic IF-THEN rules from the training data set. This new fuzzy classifier is finally applied to the well-known Wisconsin breast cancer classification problem, and a compact, interpretable and accurate probabilistic IF-THEN rule base is achieved.