Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems
Tomoharu Nakashima*, Yasuyuki Yokota*, Hisao Ishibuchi*,
Gerald Schaefer**, Aleš Drastich***, and Michal Závišek***
*Department of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan
**School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, U.K.
***Faculty of Electrical Engineering and Communication, Brno University of Technology, 61200 Brno, Královo Pole, Kolejní 4, Czech Republic
We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.
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