ODDboost: Incorporating Posterior Estimates into AdaBoost

摘要

Boosting methods while being among the best classification methods developed so far, are known to degrade performance in case of noisy data and overlapping classes. In this paper we propose a new upper generalization bound for weighted averages of hypotheses, which uses posterior estimates for training objects and is based on reduction of binary classification problem with overlapping classes to a deterministic problem. If we are given accurate posterior estimates, proposed bound is lower than existing bound by Schapire et al [25]. We design an AdaBoost-like algorithm which optimizes proposed generalization bound and show that incorporated with good posterior estimates it performs better than the standard AdaBoost on real-world data sets.