Fuzzy c-means classifier with particle swarm optimization
Hidetomo Ichihashi, Katsuhiro Honda, Akira Notsu, Keichi Ohta · 2008
Fuzzy c-means-based classifier derived from a generalized fuzzy c-means (FCM) partition and optimized by particle swarm optimization (PSO) is proposed. The procedure consists of two phases. The first phase is an unsupervised clustering, which is not initialized with random numbers, hence being deterministic. The second phase is a supervised classification. The parameters of membership functions and the location of cluster centers are optimized by the PSO and cross validation (CV) procedures. Since different types of classifiers work best for different types of data, our strategy is to parameterize the classifier and tailor it to individual data set. The FCM classifier outperforms well established methods such as k-nearest neighbor classifier (k-NN), support vector machine (SVM) and Gaussian mixture classifier (GMC) in terms of 10-fold CV and three-way data splits.