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While new and effective methods for anomaly detection are frequentlyintroduced, many studies prioritize the detection task without considering theneed for explainability. Yet, in real-world applications, anomaly explanation,which aims to provide explanation of why specific data instances are identifiedas anomalies, is an equally important task. In this work, we present a novelapproach for efficient and accurate model-agnostic anomaly explanation fortabular data using Predicate-based Association Rules (PARs). PARs can provideintuitive explanations not only about which features of the anomaly instanceare abnormal, but also the reasons behind their abnormality. Our user studyindicates that the anomaly explanation form of PARs is better comprehended andpreferred by regular users of anomaly detection systems as compared to existingmodel-agnostic explanation options. Furthermore, we conduct extensiveexperiments on various benchmark datasets, demonstrating that PARs comparefavorably to state-of-the-art model-agnostic methods in terms of computingefficiency and explanation accuracy on anomaly explanation tasks. The code forPARs tool is available at https://github.com/NSIBF/PARs-EXAD.

PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation