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Dependency-based anomaly detection: A general framework and comprehensive evaluation
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DOI：10.1016/j.eswa.2025.129249.png)
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Abstract
En 中文
• Introduces DepAD for using variable dependencies for detecting anomalies. • Reframes unsupervised detection as supervised feature selection and prediction. • Evaluates DepAD against nine state-of-the-art methods on 32 datasets. • DepAD outperforms in detecting anomalies with better interpretability. • Demonstrates new insights and interpretations for detected anomalies.
Keywords:
Anomaly detection
Dependency-based
Causal relationship
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
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Cited Papers
Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption
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