Reject Strategies Driven Combination of Pattern Classifiers

Abstract:

This article deals with the combination of pattern classifiers with two reject options. Such classifiers operate in two steps and differ on the managing of ambiguity and distance rejection (independently or not). We propose to combine the first steps of these classifiers using concepts from the theory of evidence. We propose some intelligent basic probability assignment to reject classes before using the combination rule. After combination, a decision rule is proposed for classifying or rejecting patterns either for distance or for ambiguity. We emphasize that rejection is not related to a lack of consensus between the classifiers, but to the initial reject options. In the case of ambiguity rejection, a class-selective approach has been used. Some illustrative results on artificial and real data are given.