The area under receiver operating characteristics (AUC) is the standard
measure for comparison of anomaly detectors. Its advantage is in providing a
scalar number that allows a natural ordering and is independent on a threshold,
which allows to postpone the choice. In this work, we question whether AUC is a
good metric for anomaly detection, or if it gives a false sense of comfort, due
to relying on assumptions which are unlikely to hold in practice. Our
investigation shows that variations of AUC emphasizing accuracy at low false
positive rate seem to be better correlated with the needs of practitioners, but
also that we can compare anomaly detectors only in the case when we have
representative examples of anomalous samples. This last result is disturbing,
as it suggests that in many cases, we should do active or few-show learning
instead of pure anomaly detection.

本文质疑了使用接受者操作特征下的面积（AUC）是否是衡量异常检测性能的合适标准，同时提出了在实践中可能存在的偏差性；作者发现，注重在低假阳率下的 AUC 表现良好，并且仅在具有代表性的异常样本时才能比较异常检测器。这一结果提示，在许多情况下，我们应该进行主动或少数样本学习而不是纯粹的异常检测。