Explainable Contextual Anomaly Detection using Quantile Regression Forests
This addresses the need for interpretable anomaly detection in domains where understanding deviations in specific contexts is crucial, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of detecting contextual anomalies by connecting dependency-based traditional anomaly detection with contextual methods, proposing an interpretable approach using Quantile Regression Forests. The method outperforms state-of-the-art methods in accuracy and interpretability on synthetic and real-world datasets.
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.