Multi-class AdaBoost with Hypothesis Margin

Xiaobo Jin, Xinwen Hou, Cheng-Lin Liu

Abstract: Most AdaBoost algorithms for multi-class problems have to decompose the multi-class classification into multiple binary problems, like the Adaboost.MH and the LogitBoost. This paper proposes a new multi-class AdaBoost algorithm based on hypothesis margin, called AdaBoost.HM, which directly combines multi-class weak classifiers. The hypothesis margin maximizes the output about the positive class meanwhile minimizes the maximal outputs about the negative classes. We discuss the upper bound of the training error about AdaBoost.HM and a previous multi-class learning algorithm AdaBoost.M1. Our experiments using feed forward neural networks as weak learners show that the proposed AdaBoost.HM yields higher classification accuracies than the AdaBoost.M1 and the AdaBoost.MH, and meanwhile, AdaBoost.HM is computationally efficient in training.