  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.
