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.

翻译：尽管新型有效的异常检测方法层出不穷，但许多研究侧重检测任务而忽视了解释性需求。然而在实际应用中，异常解释（旨在阐明特定数据实例被标记为异常的原因）同样至关重要。本文提出一种基于谓词关联规则（Predicate-based Association Rules, PARs）的新方法，用于对表格数据进行高效且准确的模型无关异常解释。PARs不仅能直观解释异常实例的哪些特征存在异常，还能揭示异常背后的原因。用户研究表明，相较于现有模型无关解释方案，常规异常检测系统用户更易理解且更偏好PARs提供的异常解释形式。此外，我们在多个基准数据集上开展的大量实验表明，PARs在异常解释任务的计算效率和解释准确性方面均优于当前最先进的模型无关方法。PARs工具代码已开源至https://github.com/NSIBF/PARs-EXAD。