论文标题
使用有限的用户反馈，改善黑盒顺序异常检测器相关性
Improve black-box sequential anomaly detector relevancy with limited user feedback
论文作者
论文摘要
异常检测器通常被设计为捕获统计异常。最终用户通常对所有检测到的异常值都不感兴趣，而只有与其应用相关的异常值。给定现有的黑盒顺序异常检测器，本文提出了一种使用少量人类反馈来提高其用户相关性的方法。作为我们的第一个贡献，该方法对检测器不可知：它仅假定访问其异常得分，而无需对其内部的任何其他信息。受异常为不同类型的事实的启发，我们的方法确定了这些类型，并利用用户反馈将相关性分配给类型。作为我们的第二个贡献，这种相关得分用于调整随后的异常选择过程。合成和现实世界数据集的经验结果表明，我们的方法在精度上取得了重大改进，并在一系列异常检测器上进行了回忆。
Anomaly detectors are often designed to catch statistical anomalies. End-users typically do not have interest in all of the detected outliers, but only those relevant to their application. Given an existing black-box sequential anomaly detector, this paper proposes a method to improve its user relevancy using a small number of human feedback. As our first contribution, the method is agnostic to the detector: it only assumes access to its anomaly scores, without requirement on any additional information inside it. Inspired by a fact that anomalies are of different types, our approach identifies these types and utilizes user feedback to assign relevancy to types. This relevancy score, as our second contribution, is used to adjust the subsequent anomaly selection process. Empirical results on synthetic and real-world datasets show that our approach yields significant improvements on precision and recall over a range of anomaly detectors.
