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