Smart sampling: A novel unsupervised boosting approach for outlier detection

Abstract

© Springer International Publishing AG 2016. While various ensemble algorithms have been proposed for supervised ensembles or clustering ensembles, there are few ensemble based approaches for outlier detection. The main challenge in this context is the lack of knowledge about the accuracy of the outlier detectors. Hence, none of the proposed approaches focused on sequential boosting techniques. In this paper for the first time we propose a novel boosting algorithm for outlier detection called BSS, where we sequentially improve the accuracy of each ensemble detector in an unsupervised manner. We discuss the effectiveness of our approach in terms of bias-variance tradeoff. Furthermore, an exte..