论文标题
从数据中学习不变规则，以解释异常检测
Learning Invariant Rules from Data for Interpretable Anomaly Detection
论文作者
论文摘要
在异常检测的研究领域，经常开发新颖和有希望的方法。但是，大多数现有研究仅专注于检测任务，而忽略了基本模型的解释性及其检测结果。然而，旨在解释为何将特定数据实例识别为异常的解释，这是许多现实世界中同样重要的任务。在这项工作中，我们提出了一个新颖的框架，该框架协同了几种机器学习和数据挖掘技术，以自动学习不变规则，这些规则在给定数据集中始终如一。学识渊博的不变规则可以在推理阶段提供明确的解释结果，因此对于随后关于报告异常的决策非常有用。此外，我们的经验评估表明，与启动异常检测模型相比，在公共基准数据集上，提出的方法在AUC和部分AUC方面也可以在AUC和部分AUC方面实现可比性甚至更好的性能。
In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies exclusively focus on the detection task only and ignore the interpretability of the underlying models as well as their detection results. Nevertheless, anomaly interpretation, which aims to provide explanation of why specific data instances are identified as anomalies, is an equally important task in many real-world applications. In this work, we propose a novel framework which synergizes several machine learning and data mining techniques to automatically learn invariant rules that are consistently satisfied in a given dataset. The learned invariant rules can provide explicit explanation of anomaly detection results in the inference phase and thus are extremely useful for subsequent decision-making regarding reported anomalies. Furthermore, our empirical evaluation shows that the proposed method can also achieve comparable or even better performance in terms of AUC and partial AUC on public benchmark datasets across various application domains compared with start-of-the-art anomaly detection models.
