The hybrid genetic fuzzy C-means: a reasoned implementation

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 integration 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 performance evaluation results on several public benchmark databases. Results obtained show the efficiency of GFCM algorithm.

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