On unifying probabilistic/fuzzy and possibilistic rejection-based classifiers

Abstract

This paper aims at unifying the presentation of two-fold rejection-based pattern classifiers. We propose to define such a classifier as a couple of labelling and hardening functions which are independent in some way. Within this framework, crisp and probabiIistic/fuzzy rejection-based classifiers are shown to be particular cases of possibilistic ones. Classifiers with no reject option remains particular cases of rejection-based ones. Examples of so-defined classifiers are presented and their ability to deal with the reject problem is shown on artificial and real data sets.