Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual features and behavioral features. In this paper, we develop connections between dependency-based traditional anomaly detection methods and contextual anomaly detection methods. Based on resulting insights, we propose a novel approach to inherently interpretable contextual anomaly detection that uses Quantile Regression Forests to model dependencies between features. Extensive experiments on various synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art anomaly detection methods in identifying contextual anomalies in terms of accuracy and interpretability.

翻译：传统异常检测方法通过平等对待所有特征来识别偏离大多数对象的样本。而上下文异常检测方法则通过将特征划分为上下文特征与行为特征，旨在检测在相似对象构成的上下文中偏离其他对象的异常样本。本文建立了基于依赖关系的传统异常检测方法与上下文异常检测方法之间的关联。基于此洞见，我们提出一种新型的内在可解释上下文异常检测方法，利用分位数回归森林对特征间的依赖关系进行建模。在多种合成与真实数据集上的大量实验表明，该方法在识别上下文异常方面的准确性与可解释性均优于当前最先进的异常检测方法。