Genetic fuzzy classifier for benchmark cancer diagnosis

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Original languageEnglish
Pages (from-to)1063-1067
Journal / PublicationIECON Proceedings (Industrial Electronics Conference)
Publication statusPublished - 1997


TitleProceedings of the 1997 23rd Annual International Conference on Industrial Electronics, Control, and Instrumentation, IECON. Part 2 (of 4)
CityNew Orleans, LA, USA
Period9 - 14 November 1997


An effective fuzzy classifier is proposed for solving a benchmark cancer diagnosis problem. This system comprises the use of optimized fuzzy membership functions through Genetic Algorithms, while the associated rules are generated from numerical data. In addition, a modified nearest-neighbour method is recommended to remedy the drawback of rules confinement. The end result convinces that this approach has the ability to handle classification problems with large data dimension.