Dynamic classifier selection based on imprecise probabilities : a case study for the naive Bayes classifier Ghent University
Dynamic classifier selection is a classification technique that, for every new instance to be classified, selects and uses the most competent classifier among a set of available ones. In this way, a new classifier is obtained, whose accuracy often outperforms that of the individual classifiers it is based on. We here present a version of this technique where, for a given instance, the competency of a classifier is based on the robustness of its ...