On Using the Shapley Value for Anomaly Localization: A Statistical Investigation
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Abstract (arXiv)
Recent publications have suggested using the Shapley value for anomaly localization for sensor data systems. Using a reasonable mathematical anomaly model for full control, experiments indicate that using a single fixed term in the Shapley value calculation achieves a lower complexity anomaly localization test, with the same probability of error, as a test using the Shapley value for all cases tested. A proof demonstrates these conclusions must be true for all independent observation cases. For dependent observation cases, no proof is available.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 28 Jul 2025.
Builds on
8 of 23 references.
References · 8
- 2019Deep Learning for Anomaly Detection: A Surveycited by 358
- 2019The many Shapley values for model explanationcited by 344
- 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 208
- 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
- 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Valuescited by 36
- 2019Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detectioncited by 19
- 2023Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Modelcited by 10
- 2020A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scorescited by 2
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Citing papers per year: 2026: 1.
1 paper.