Online Boosting Algorithms Based on Exponential and 0-1 Loss
Ji Hou · Acta Automatica Sinica · 2014
In this paper, strict derivation for the online form of Boosting algorithms using exponential loss and 0-1 loss is presented, which proves that the two online Boosting algorithms can maximize the average margin and minimize the margin variance. By estimating the margin mean and variance incrementally, Boosting algorithms can be applied to online learning problems without losing classification accuracy. Experiments on UCI machine learning datasets show that the online Boosting using exponential loss is as accurate as batch AdaBoost, and significantly outperforms the traditional online Boosting, and that the online Boosting using 0-1 loss can minimize classification errors of positive samples and negative samples at the same time, thus applies to imbalance data. Moreover, Boosting using 0-1 loss is more robust on noisy data.