论文标题
基于沙普利价值的异常分数归因的特征功能
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
论文作者
论文摘要
在异常检测中，不规则程度通常被总结为实现的异常评分。我们解决了将这种异常得分归因于输入特征以解释异常检测结果的问题。我们特别研究了沙普利值在归因于半监督检测方法的异常得分中的使用。我们提出了一种专门设计用于归因异常得分的特征函数。这个想法是通过在局限性的特征方面局部最大程度地减少异常得分来近似某些功能。我们研究了提出的特征函数和其他通用方法的适用性，以解释多个数据集和多个异常检测方法上的异常得分。结果表明归因方法的潜在效用，包括提出的方法。
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.
