Resumen de A principled two-step method for example-dependent cost binary classification

Javier Mediavilla, Aitor Gutiérrez, Marcelino Lázaro, Aníbal Ramón Figueiras Vidal

• 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.