Anomaly detection with semi-supervised classification based on risk
  estimators
Le Thi Khanh Hien Sukanya Patra Souhaib Ben Taieb
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 strong evidence of the
effectiveness of the risk-based anomaly detection methods.