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

Date

2004-01-01

Author

Kilic, K
Uncu, O
Turksen, IB

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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.

URI

https://hdl.handle.net/11511/66357

Journal

AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS

Collections

Department of Industrial Engineering, Article

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Citation Formats

K. Kilic, O. Uncu, and I. Turksen, “A comparative analysis of fuzzy system modelling approaches: A case in mining medical diagnostic rules,” AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, pp. 1194–1199, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66357.