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Paper details
Number 2 - June 2003
Volume 13 - 2003
A fuzzy if-then rule-based nonlinear classifier
Jacek Łęski
Abstract
This paper introduces a new classifier design method that is based on a modification of the classical Ho-Kashyap procedure. The proposed method uses the absolute error, rather than the squared error, to design a linear classifier. Additionally, easy control of the generalization ability and robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixture-of-experts technique is presented. Each expert is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang form. Finally, examples are given to demonstrate the validity of the introduced method.
Keywords
classifier design, fuzzy if-then rules, generalization control, mixture of experts