Shedding light on the asymmetric learning capability of AdaBoost

In this paper, we propose a different insight to analyze AdaBoost. This analysis reveals that, beyond some preconceptions, AdaBoost can be directly used as an asymmetric learning algorithm, preserving all its theoretical properties. A novel class-conditional description of AdaBoost, which models the actual asymmetric behavior of the algorithm, is presented.

► New insight to analyze the real asymmetric learning capabilities of AdaBoost. ► A novel class-conditional interpretation of the error bound is presented. ► No changes in the weight updating rule are needed; the key to asymmetry lies in how the weights are initialized.