A weighted fuzzy classifier and its application to image processing tasks

Authors:
Tomoharu Nakashima;Gerald Schaefer;Yasuyuki Yokota;Hisao Ishibuchi
Affiliations:
College of Engineering, Osaka Prefecture University, Japan;School of Engineering and Applied Science, Aston University, UK;College of Engineering, Osaka Prefecture University, Japan;College of Engineering, Osaka Prefecture University, Japan
Venue:
Fuzzy Sets and Systems
Year:
2007

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Abstract

Many image processing applications involve a pattern classification stage. In this paper we propose a classifier based on fuzzy if-then rules that allows the incorporation of weighted training patterns which can be used to adjust the sensitivity of the classification with respect to certain classes. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty are determined from the compatibility and weights of training patterns. We also introduce a learning method which adjusts the degree of certainty in order to provide improved classification performance and reduced costs. Experimental results on several image processing tasks demonstrate the efficacy of the proposed method.