Paper:
Language: English:

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

Received: January 23, 2007

Accepted: March 19, 2007

Keywords: cost-sensitive approach, data mining, fuzzy rule-based systems, fuzzy if-then rules, pattern classification

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.11, No.6 pp. 546-553, 2007

Abstract

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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