Ensemble algorithms for unsupervised anomaly detection

Abstract

Many anomaly detection algorithms have been proposed in recent years, including density-based and rank-based algorithms. In this paper, we propose ensemble methods to improve the performance of these individual algorithms. We evaluate approaches that use score and rank aggregation for these algorithms. We also consider sequential methods in which one detection method is followed by the other. We use several datasets to evaluate the performance of the proposed ensemble methods. Our results show that sequential methods significantly improve the ability to detect anomalous data points.