Performance evaluation of fuzzy rule-based systems with class priority for medical diagnosis problems

Tomoharu Nakashima*, Yasuyuki Yokota, Gerald Schaefer, Hisao Ishibuchi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

In this paper we examine the performance of fuzzy rule-based systems with classification priority for medical diagnosis problems. The assumption in this paper is that a classification priority is given a priori for each class in a pattern classification problem. Our fuzzy rule-based system consists of a set of fuzzy if-then rules that are automatically generated from a set of given training patterns. The consequent class of fuzzy if-then rules are decided based on the number of covered training patterns for each class. We apply the fuzzy classifier with class priority to two medical diagnosis problems: appendix diagnosis and breast cancer diagnosis, and compare its performance with that of a conventional fuzzy rule-based systems.

Original languageEnglish
Title of host publication21st European Conference on Modelling and Simulation: Simulations in United Europe, ECMS 2007
Pages283-288
Number of pages6
Publication statusPublished - 1 Dec 2007
Event21st European Conference on Modelling and Simulation, ECMS 2007 - Prague, United Kingdom
Duration: 4 Jun 20076 Jun 2007

Conference

Conference21st European Conference on Modelling and Simulation, ECMS 2007
CountryUnited Kingdom
CityPrague
Period4/06/076/06/07

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    Nakashima, T., Yokota, Y., Schaefer, G., & Ishibuchi, H. (2007). Performance evaluation of fuzzy rule-based systems with class priority for medical diagnosis problems. In 21st European Conference on Modelling and Simulation: Simulations in United Europe, ECMS 2007 (pp. 283-288)