Bhattacharyya boosting

In this paper, we discuss a new feature selection criterion in boosting. Our method directly optimizes Bhattacharyya distance between the weighted positive and negative samples to find the feature vector instead of brute-force selection from a feature pool. Unlike some similar work including FisherBoost and MRCBoost, the new criterion connects the feature selection process with cost function minimization. Thus the coefficients of the member classifiers and sample distribution update are both theoretically accordant with feature selection. Our criterion can be regarded as a generalized version of FisherBoost and MRCBoost. The experiments on several data sets validate our algorithm¿s effectiveness on both training and testing sets.