PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation
This work addresses the need for explainability in anomaly detection systems, particularly for regular users in real-world applications, though it is incremental as it builds on existing model-agnostic approaches.
The authors tackled the problem of providing explainable anomaly detection for tabular data by introducing Predicate-based Association Rules (PARs), which offer intuitive explanations for why instances are flagged as anomalies, and demonstrated through experiments that PARs achieve competitive efficiency and accuracy compared to state-of-the-art model-agnostic methods.
While new and effective methods for anomaly detection are frequently introduced, many studies prioritize the detection task without considering the need for explainability. Yet, in real-world applications, anomaly explanation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally important task. In this work, we present a novel approach for efficient and accurate model-agnostic anomaly explanation for tabular data using Predicate-based Association Rules (PARs). PARs can provide intuitive explanations not only about which features of the anomaly instance are abnormal, but also the reasons behind their abnormality. Our user study indicates that the anomaly explanation form of PARs is better comprehended and preferred by regular users of anomaly detection systems as compared to existing model-agnostic explanation options. Furthermore, we conduct extensive experiments on various benchmark datasets, demonstrating that PARs compare favorably to state-of-the-art model-agnostic methods in terms of computing efficiency and explanation accuracy on anomaly explanation tasks. The code for PARs tool is available at https://github.com/NSIBF/PARs-EXAD.