We propose the novel concept of anomaly-free regions (AFR) to improve anomaly detection. An AFR is a region in the data space for which it is known that there are no anomalies inside it, e.g., via domain knowledge. This region can contain any number of normal data points and can be anywhere in the data space. AFRs have the key advantage that they constrain the estimation of the distribution of non-anomalies: The estimated probability mass inside the AFR must be consistent with the number of normal data points inside the AFR. Based on this insight, we provide a solid theoretical foundation and a reference implementation of anomaly detection using AFRs. Our empirical results confirm that anomaly detection constrained via AFRs improves upon unconstrained anomaly detection. Specifically, we show that, when equipped with an estimated AFR, an efficient algorithm based on random guessing becomes a strong baseline that several widely-used methods struggle to overcome. On a dataset with a ground-truth AFR available, the current state of the art is outperformed.

本研究解决了现有异常检测方法中对正常数据分布估计不准确的问题。通过提出“无异常区域”（AFR）的新概念，研究表明AFR能够有效约束正常数据的概率分布，从而显著提高异常检测的效果。实验结果显示，利用AFR的检测方法在许多流行的异常检测技术面前展现出更强的表现。 

通过无异常区域约束异常检测