Dependency-based anomaly detection: A general framework and comprehensive evaluation
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(Published version)
Date
2026
Authors
Lu, S.
Liu, L.
Yu, K.
Le, T.D.
Liu, J.
Li, J.
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Expert Systems With Applications, 2026; 297(129249):129249-129249
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Anomaly detection is crucial for identifying unusual behaviors in data, which often provide valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a general and modular framework that leverages variable dependencies to uncover meaningful anomalies with improved interpretability. DepAD reframes unsupervised anomaly detection as a sequence of supervised feature selection and prediction tasks, enabling users to tailor detection methods to their data and domain. We systematically evaluate 125 DepAD algorithm variants across 32 real-world datasets, combining different off-the-shelf feature selection and prediction techniques. We compare DepAD with twelve state-of-the-art anomaly detection methods and demonstrate its consistent superior performance. Furthermore, we demonstrate that DepAD provides intuitive and informative interpretations of detected anomalies, highlighting its utility in practical applications.
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Copyright 2025 The Authors. (http://creativecommons.org/licenses/by/4.0/)
Access Condition Notes: This is an open access article under the CC BY license