Calibrating AdaBoost for Asymmetric Learning

Asymmetric classification problems are characterized by class imbalance or unequal costs for different types of misclassifications. One of the main cited weaknesses of AdaBoost is its perceived inability to handle asymmetric problems. As a result, a multitude of asymmetric versions of AdaBoost have been proposed, mainly as heuristic modifications to the original algorithm. In this paper we challenge this approach and propose instead handling asymmetric tasks by properly calibrating the scores of the original AdaBoost so that they correspond to probability estimates. We then account for the asymmetry using classic decision theoretic approaches. Empirical comparisons of this approach against the most representative asymmetric Adaboost variants show that it compares favorably. Moreover, it retains the theoretical guarantees of the original AdaBoost and it can easily be adjusted to account for changes in class imbalance or costs without need for retraining.