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Shapley values analyzed for sensor anomaly detection

A new paper analyzes the use of Shapley values for localizing sensor anomalies, comparing their performance against simpler anomaly detection methods. The research proves that for independent sensor observations, the Shapley value approach is equivalent to a less complex method. However, for dependent observations, the Shapley value can be either superior or inferior depending on specific statistical conditions, suggesting potential for combined approaches. AI

IMPACT Provides theoretical insights into anomaly detection methods, potentially improving robustness in sensor systems.

RANK_REASON The cluster contains an academic paper detailing theoretical statistical analysis of a machine learning technique.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Xubin Fang, Rick S. Blum ·

    Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifiers

    arXiv:2606.00867v1 Announce Type: new Abstract: Recent publications have suggested using the Shap- ley value for sensor anomaly/attack localization. We study the performance of such an approach by using mathematically de- fined optimum binary classifiers in the Shapley value calc…

  2. arXiv stat.ML TIER_1 English(EN) · Rick S. Blum ·

    Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifiers

    Recent publications have suggested using the Shap- ley value for sensor anomaly/attack localization. We study the performance of such an approach by using mathematically de- fined optimum binary classifiers in the Shapley value calculation. To judge localization performance, we s…