Multiclass Boosting for Weak Classifiers

Abstract

AdaBoost.M2 is a boosting algorithm designed for multiclass problems with weak base classifiers. The algorithm is designed to minimize a very loose bound on the training error. We propose two alternative boosting algorithms which also minimize bounds on performance measures. These performance measures are not as strongly connected to the expected error as the training error, but the derived bounds are tighter than the bound on the training error of AdaBoost.M2. In experiments the methods have roughly the same performance in minimizing the training and test error rates. The new algorithms have the advantage that the base classifier should minimize the confidence-rated error, whereas for AdaBoost.M2 the base classifier should minimize the pseudo-loss. This makes them more easily applicable to already existing base classifiers. The new algorithms also tend to converge faster than AdaBoost.M2.