Improving Anomaly Discovery through Active Learning with Tree-based Ensembles

Tree-based anomaly detection ensembles are naturally suited for active learning, and the greedy querying strategy of seeking labels for instances with the highest anomaly scores is an efficient approach. Novel batch and streaming active learning algorithms are developed to improve the diversity of discovered anomalies and handle data drift, respectively.