We propose the Autoencoding Binary Classifiers (ABC), a novel supervised
anomaly detector based on the Autoencoder (AE). There are two main approaches
in anomaly detection: supervised and unsupervised. The supervised approach
accurately detects the known anomalies included in training data, but it cannot
detect the unknown anomalies. Meanwhile, the unsupervised approach can detect
both known and unknown anomalies that are located away from normal data points.
However, it does not detect known anomalies as accurately as the supervised
approach. Furthermore, even if we have labeled normal data points and
anomalies, the unsupervised approach cannot utilize these labels. The ABC is a
probabilistic binary classifier that effectively exploits the label
information, where normal data points are modeled using the AE as a component.
By maximizing the likelihood, the AE in the proposed ABC is trained to minimize
the reconstruction error for normal data points, and to maximize it for known
anomalies. Since our approach becomes able to reconstruct the normal data
points accurately and fails to reconstruct the known and unknown anomalies, it
can accurately discriminate both known and unknown anomalies from normal data
points. Experimental results show that the ABC achieves higher detection
performance than existing supervised and unsupervised methods.

本文提出了 Autoencoding Binary Classifiers（ABC），一种基于自编码器的新型监督异常检测器。ABC 是一种概率二元分类器，可以有效利用标签信息，并且以编码顺序优化模型对正常数据点的再现误差最小化，对已知异常最大化再现误差以实现对已知和未知异常的准确鉴别。实验结果表明，ABC 具有比现有监督和无监督方法更高的检测性能。

用自编码器二分类器实现有监督的异常检测

Autoencoding Binary Classifiers for Supervised Anomaly Detection

We present a novel subset scan method to detect if a probabilistic binary
classifier has statistically significant bias -- over or under predicting the
risk -- for some subgroup, and identify the characteristics of this subgroup.
This form of model checking and goodness-of-fit test provides a way to
interpretably detect the presence of classifier bias or regions of poor
classifier fit. This allows consideration of not just subgroups of a priori
interest or small dimensions, but the space of all possible subgroups of
features. To address the difficulty of considering these exponentially many
possible subgroups, we use subset scan and parametric bootstrap-based methods.
Extending this method, we can penalize the complexity of the detected subgroup
and also identify subgroups with high classification errors. We demonstrate
these methods and find interesting results on the COMPAS crime recidivism and
credit delinquency data.

介绍了一种新颖的子集扫描方法，用于检测统计学显著的偏差或区域，以及识别分类器偏差或预测不良的特征子集，该方法使用基于子集扫描和参数引导的方法处理了指数级可能的子集。