An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
Romain Hermary, Nesryne Mejri, Djamila Aouada
cs.LG, stat.ML
Submitted: 2026-07-24
Comments: Published in EUVIP 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging.
Terminology
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.
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