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Designing Cost-sensitive Fuzzy Classification Systems Using Rule-weight

Authors:
Mansoor Zolghadri Jahromi
Mohammad Reza Moosavi

Keywords: Fuzzy Classification Systems; Cost Sensitive Classification; Rule Weight; Data Mining

Abstract:
In the field of pattern classification, we often encounter problems that class-to-class misclassification costs are not the same. For example, in the medical domain, misclassifying a patient as normal is often much more costly than misclassifying a normal as patient. Our aim in this paper is to propose a method of designing fuzzy rule-based classification systems to tackle this problem. We use rule-weight as a simple mechanism to tune the rule-base. Assuming that class-to-class misclassification costs are known, we propose a learning algorithm that attempts to minimize the total cost of the classifier on train data (i.e., instead of minimizing the error-rate). Using a number of UCI datasets we show that the method is quite effective in reducing the average cost of the classifier on test data.

Pages: 168 to 173

Copyright: Copyright (c) IARIA, 2011

Publication date: October 23, 2011

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-162-5

Location: Barcelona, Spain

Dates: from October 23, 2011 to October 29, 2011