Feature combination using boosting

Combining all features coded by different systems can improve the performance of a classification system. In this paper, a strategy of boosting based feature combination is introduced, where a variant of boosting is proposed for integrating different features. Different from the general boosting, at each round of this variant boosting, some weak classifiers are built on different feature sets, one of which is trained on one feature set. And then these classifiers are combined by weighted voting into a single one as the output classifier of this round. Experiments on classification of three UCI data sets and handwritten digit recognition indicate that this variant of boosting is a promising learning algorithm for feature combination. To some extent, this strategy of feature combination can integrate feature selection, feature communication, and classifier learning in its learning procedure.