A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores

In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for interpreting the results of anomaly detection. We particularly investigate the use of the Shapley value for attributing...

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Bibliographic Details
Published inarXiv.org
Main Authors Takeishi, Naoya, Kawahara, Yoshinobu
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.02.2023
Subjects
Anomalies
Characteristic functions
Game theory
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Summary:In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for interpreting the results of anomaly detection. We particularly investigate the use of the Shapley value for attributing anomaly scores of semi-supervised detection methods. We propose a characteristic function specifically designed for attributing anomaly scores. The idea is to approximate the absence of some features by locally minimizing the anomaly score with regard to the to-be-absent features. We examine the applicability of the proposed characteristic function and other general approaches for interpreting anomaly scores on multiple datasets and multiple anomaly detection methods. The results indicate the potential utility of the attribution methods including the proposed one.
ISSN:2331-8422