Raúl Santos-Rodríguez, Universidad Carlos III de Madrid, Spain
Rocío Alaiz-Rodríguez, Universidad de León, Spain
Alicia Guerrero-Curieses, Universidad Rey Juan Carlos, Spain
Jesús Cid-Sueiro, Universidad Carlos III de Madrid, Spain

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Abstract

This paper analyzes the application of a particular class of Bregman divergences to design cost-sensitive classifiers for multiclass problems. We show that these divergence measures can be used to estimate posterior probabilities with maximal accuracy for the probability values that are close to the decision boundaries. Asymptotically, the proposed divergence measures provide classifiers minimizing the sum of decision costs in non-separableproblems, and maximizing a margin in separable MAP problems.