Anomaly detection with semi-supervised classification based on risk estimators

Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb

Abstract: A significant limitation of one-class classification anomaly detection methods is their reliance on the assumption that unlabeled training data only contains normal instances. To overcome this impractical assumption, we propose two novel classification-based anomaly detection methods. Firstly, we introduce a semi-supervised shallow anomaly detection method based on an unbiased risk estimator. Secondly, we present a semi-supervised deep anomaly detection method utilizing a nonnegative (biased) risk estimator. We establish estimation error bounds and excess risk bounds for both risk minimizers. Additionally, we propose techniques to select appropriate regularization parameters that ensure the nonnegativity of the empirical risk in the shallow model under specific loss functions. Our extensive experiments provide evidence of the effectiveness of the risk-based anomaly detection methods.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: Following the AE' comments, we have edited our paper. The main change includes: we added more baseline methods and improved the experiment section.

Code: https://github.com/LeThiKhanhHien/rAD

Assigned Action Editor: ~Tao_Qin1

Submission Number: 1763