Incorporating Feedback into Tree-based Anomaly Detection

30 Aug 2017Shubhomoy Das • Weng-Keen Wong • Alan Fern • Thomas G. Dietterich • Md Amran Siddiqui

Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective. Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest.

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