Supervised fuzzy clustering for the identification of fuzzy classifiers

The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper a new fuzzy model structure is proposed where each rule can represent more than one classes with different probabilities. The obtained classifier can be considered as an extension of the quadratic Bayes classifier that utilizes mixture of models for estimating the class conditional densities. A supervised clustering algorithm has been worked out for the identification of this fuzzy model. The relevant input variables of the fuzzy classifier have been selected based on the analysis of the clusters by Fisher’s interclass separability criteria. This new approach is applied to the well-known wine and Wisconsin Breast Cancer classification problems.