An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
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Computer Science > Machine Learning
Title:An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
Abstract:Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics such as AUROC, AUPR, F1-score, and MCC are widely used, their values convey different meanings depending on the anomaly ratio. In this work, we analyse the behaviour of those four common anomaly detection metrics under varying levels of imbalance. We focus on the study of metric landscapes, visualisations that relate metric values to true positive and true negative rates, providing an intuitive view of metric preferences and stability. Our analysis offers practical guidance for interpreting and comparing anomaly detection results across datasets with different imbalance ratios.
| Comments: | Published in EUVIP 2026 |
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2607.22286 [cs.LG] |
| (or arXiv:2607.22286v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.22286
arXiv-issued DOI via DataCite (pending registration)
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