The hybrid genetic fuzzy C-means: a reasoned implementation
Alessandro G. Di Nuovo, Vincenzo Catania, Maurizio Palesi · 2006
Abstract:- In this paper we present an hybrid approach which integrate Fuzzy C-Means (FCM) algorithms and Genetic Algorithms (GAs) to design an optimal classifier for the specific classification problem. This in-tegration allows automatic generation of an classifier system, with an optimized subset of features, from a database of examples. The generated classifier strongly outperform the classic FCM algorithm. A reasoned implementation of the hybrid algorithm, we called GFCM, is given along with a comparative study and per-formance evaluation results on several public benchmark databases. Results obtained show the efficiency of GFCM algorithm.