Gated Boosting: Efficient Classifier Boosting and Combining

Abstract

We study boosting by using a gating mechanism, Gated Boosting, to perform resampling instead of the weighting mechanism used in Adaboost. In our method, gating networks determine the distribution of the samples for training a consecutive base classifier, considering the predictions of the prior base classifiers. Using gating networks prevents the training instances from being repeatedly included in different subsets used for training base classifiers, being a key goal in achieving diversity. Furthermore, these are the gating networks that determine which classifiers’ output to be pooled for producing the final output. The performance of the proposed method is demonstrated and compared to Adaboost on four benchmarks from the UCI repository, and MNIST dataset.