Dealing with Uncertainty in Contextual Anomaly Detection
This work addresses uncertainty in anomaly detection for domains like healthcare, where reliable assessments are critical, though it appears incremental as it builds on existing methods.
The paper tackles the problem of uncertainty in contextual anomaly detection by proposing a normalcy score framework that models aleatoric and epistemic uncertainties, demonstrating improved detection accuracy and interpretability over state-of-the-art methods on benchmark datasets and a real-world cardiology application.
Contextual anomaly detection (CAD) aims to identify anomalies in a target (behavioral) variable conditioned on a set of contextual variables that influence the normalcy of the target variable but are not themselves indicators of anomaly. In many anomaly detection tasks, there exist contextual variables that influence the normalcy of the target variable but are not themselves indicators of anomaly. In this work, we propose a novel framework for CAD, normalcy score (NS), that explicitly models both the aleatoric and epistemic uncertainties. Built on heteroscedastic Gaussian process regression, our method regards the Z-score as a random variable, providing confidence intervals that reflect the reliability of the anomaly assessment. Through experiments on benchmark datasets and a real-world application in cardiology, we demonstrate that NS outperforms state-of-the-art CAD methods in both detection accuracy and interpretability. Moreover, confidence intervals enable an adaptive, uncertainty-driven decision-making process, which may be very important in domains such as healthcare.