Upper entropy of credal sets. Applications to credal classification

We present an application of the measure of entropy for credal sets: as a branching criterion for constructing classification trees based on imprecise probabilities which are determined with the imprecise Dirichlet model. We also justify the use of upper entropy as a global uncertainty measure for credal sets and present a deduction of this measure. We have carried out several experiments in which credal classification trees are built taking a global uncertainty measure as a basis. The results show how the introduced methodology improves the performance of traditional methods (Naive Bayes and C4.5), by providing a much lower error rate.