Anomaly Detection with Partially Observed Anomaly Types

In this paper, we consider the problem of anomaly detection when a small number of anomaly types are observed. Previous research primarily focused on supervised learning, when all samples are labeled. And unsupervised learning is used, when all samples are unlabeled. However, many settings do not satisfy the above two situations. Recently, there are some studies on the situations that anomalies are partially observed (e.g., Anomaly Detection with partially Observed Anomalies). It is generally believed that the anomalies are classifiable in these studies. And it is common that the types of observed anomalies cannot include all types of anomalies in the case of partially observed anomalies. We refer to this problem as anomaly detection with partially observed anomaly types and propose a two-stage anomaly detection algorithm in this condition. The proposed method in this paper is based on Anomaly Detection with partially Observed Anomalies and is available in the new setting. Experimental results demonstrate the effectiveness of the proposed method both in the case of insufficient types of observed anomalies and in the case of sufficient types of observed anomalies. Besides, anomaly detection with partially observed anomaly types avoids the use of hyper-parameter and has high generality in different datasets.