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：传统异常检测方法旨在通过平等对待所有特征来识别偏离大多数其他对象的异常点。相比之下，上下文异常检测方法将特征划分为上下文特征和行为特征，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点。本文建立了基于依赖性的传统异常检测方法与上下文异常检测方法之间的联系。基于所得洞察，我们提出了一种新颖的、本质可解释的上下文异常检测方法，该方法利用分位数回归森林对特征间的依赖关系进行建模。在多种合成数据集和真实世界数据集上的广泛实验表明，与现有最优异常检测方法相比，我们的方法在识别上下文异常方面的准确性和可解释性均更优。