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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21023 |
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| _version_ | 1866917494794485760 |
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| author | Fang, Xubin Blum, Rick S. Freytag, Franziska |
| author_facet | Fang, Xubin Blum, Rick S. Freytag, Franziska |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Using the Shapley Value for Anomaly Localization: A Statistical Investigation Fang, Xubin Blum, Rick S. Freytag, Franziska Machine Learning Signal Processing 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. |
| title | On Using the Shapley Value for Anomaly Localization: A Statistical Investigation |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2507.21023 |