Application of the conditional fuzzy clustering with prototypes pairs to classification

Abstract

In the paper the conditional fuzzy clustering algorithm dedicated to classification methods is proposed. Three methods determining conditional variable values are presented. The clustering is applied to the nonlinear extension of the IRLS classifier, which uses different loss functions. Classification quality and computing time achieved for six benchmark datasets are compared with the Lagrangian SVM method.