A comparative analysis of fuzzy system modelling approaches: a case in mining medical diagnostic rules

Authors:
Kemal Kılıç;Özge Uncu;I. B. Türkşen
Affiliations:
FENS, Sabancı University, Istanbul, Turkey;Dept of IE, Middle East Technical University, Ankara, Turkey;MIE, University of Toronto, Toronto, ON, Canada
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Information Sciences: an International Journal - Special issue: Medical expert systems

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Abstract

Fuzzy system modeling approximates highly nonlinear systems by means of fuzzy if-then rules In the literature, different approaches are proposed for mining fuzzy if-then rules from historical data These approaches usually utilize fuzzy clustering in structure identification phase In this research, we are going to analyze three possible approaches from the literature and try to compare their performances in a medical diagnosis classification problem, namely Aachen Aphasia Test Given the fact that the comparison is conducted on a single data set; the conclusions are by no means inclusive However, we believe that the results might provide some valuable insights.