Anomaly detection with semi-supervised classification based on risk estimators
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 1 Sep 2023.
Builds on
17 of 58 references.
References · 17
- 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
- 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
- 2019Deep Semi-Supervised Anomaly Detectioncited by 291
- 2022ADBench: Anomaly Detection Benchmarkcited by 232
- 2017Positive-Unlabeled Learning with Non-Negative Risk Estimatorcited by 219
- 2018Learning from positive and unlabeled data: a surveycited by 219
- 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
- 2022ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functionscited by 153
- 2020COPOD: Copula-Based Outlier Detectioncited by 146
- 2018Anomaly Detection using One-Class Neural Networkscited by 82
- 2014Toward Supervised Anomaly Detectioncited by 73
- 2013One-Class Classification: Taxonomy of Study and Review of Techniquescited by 68
5 more
- 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
- 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
- 2018An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videoscited by 45
- 2016Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learningcited by 41
- 2018Classification from Positive, Unlabeled and Biased Negative Datacited by 34
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.