A Principled Two-Step Method for Example-Dependent Cost Binary Classification.

This paper presents a principled two-step method for example-dependent cost binary classification problems. The first step obtains a consistent estimate of the posterior probabilities by training a Multi-Layer Perceptron with a Bregman surrogate cost. The second step uses the provided estimates in a Bayesian decision rule. When working with imbalanced datasets, neutral re-balancing allows getting better estimates of the posterior probabilities. Experiments with real datasets show the good performance of the proposed method in comparison with other procedures.