In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.
@article{arxiv.2210.06959,
title = {A Survey on Explainable Anomaly Detection},
author = {Zhong Li and Yuxuan Zhu and Matthijs van Leeuwen},
journal= {arXiv preprint arXiv:2210.06959},
year = {2023}
}
Comments
Paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD) for publication (preprint version)