This paper illustrated an evolutionary algorithm which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community.
Learning fuzzy classifiers with evolutionary algorithms / M. Beretta, A.G.B. Tettamanzi - In: Soft computing applications / [a cura di] A. Bonarini, F. Masulli, G. Pasi. - Heidelberg : Physica-Verlag, 2003. - ISBN 3790815446. - pp. 1-10 (( Intervento presentato al 4. convegno Italian Workshop on Fuzzy Logic (WILF) tenutosi a Milano nel 2001.
Learning fuzzy classifiers with evolutionary algorithms
A.G.B. Tettamanzi
2003
Abstract
This paper illustrated an evolutionary algorithm which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community.
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