Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning

arXiv:1901.08930v2 Announce Type: replace
Abstract: In many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensemble detectors based on label feedback allows us to …

abstract active learning analyst anomaly applications arxiv computer computer security cs.lg discovery false false positives fraud fraud prevention human insights labels prevention security stat.ml streaming tree true type world