Parameter Optimization by Genetic Algorithm for FCM Classifier

Abstract

A fuzzy c-means (FCM) classifier derived from a generalized FCM clustering has been proposed. In this paper, the classifier is not initialized with random numbers, hence being deterministic. The parameters are optimized by cross validation (CV) protocol and the simple genetic algorithm. The FCM classifier outperforms well established methods such as the support vector machine and the k-nearest neighbor classifier in terms of generalization ability.