Abstract:In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label -true positive (TP) or false positive (FP). In this paper, we propose a method (called OMD-Clustering) that exploits this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to investigate them. The OMD-Clustering method iteratively suggests the top-1 anomalous instance to a hum… Show more
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