提出新方法同时建模两类不确定性，提升异常检测可靠性。
Dealing with Uncertainty in Contextual Anomaly Detection
- 基于异方差高斯过程，将Z-score视为随机变量
- 在基准数据集和心脏病学应用中准确率优于现有方法
- 提供置信区间，支持医疗等高风险领域自适应决策
上下文异常检测（CAD）旨在根据影响目标变量正常性的上下文变量，识别目标变量的异常。在许多异常检测任务中，上下文变量虽影响正常性，但本身并非异常指标。本文提出一种新型框架——正常度评分（NS），显式建模了偶然性不确定性和认知不确定性。该方法基于异方差高斯过程回归，将Z-score视为随机变量，从而生成反映异常评估可靠性的置信区间。在多个基准数据集及心脏病学实际应用中，实验表明NS在检测准确率与可解释性方面均优于现有先进方法。此外，置信区间支持基于不确定性的自适应决策过程，在医疗等关键领域具有重要意义。
原文摘要 · Abstract (English)
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.
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