Set-valued Bayesian Inference with Probabilistic Equivalence

In this paper, a unified view of the problem of class-selection with Bayesian classifiers is presented. Selecting a subset of classes instead of singleton allows 1) to reduce the error rate and 2) to propose a reduced set to another classifier or an expert. This second step provides additional information, and therefore increases the quality of the result. The proposed framework, based on the evaluation of the probabilistic equivalence, allows to retrieve the class-selective frameworks that have been proposed in the literature. Several experiments show the effectiveness of this generic proposition.