AutoOD: Automatic Outlier Detection
AutoOD: Automatic Outlier Detection
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DOI：
10.1145/3588700
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发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Yu Wang;Yu Wang
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文献类型：
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作者：
Yu Wang;Yu Wang
异常值检测在现实世界中至关重要。由于存在许多异常值检测技术，这些技术通常对同一数据集返回不同的结果，因此用户必须解决确定这些技术中哪一种最适合其任务并调整其参数的问题。这在无监督环境中尤其具有挑战性，因为没有标签可用于此类方法和参数优化所需的交叉验证。在这项工作中，我们提出了 AutoOD，它使用现有的无监督检测技术来自动生成高质量的异常值，而无需任何人工调整。 AutoOD 的全新策略将无监督异常值检测和监督分类的优点统一在一个集成解决方案中。它在目标数据集上自动测试一组不同的无监督异常值检测器，从其组合检测结果中提取有用信号，以可靠地捕获异常值和异常值之间的关键差异。然后，它使用这些信号生成“自定义离群值分类器”来对离群值进行分类，其准确性与使用真实标签训练的监督离群值分类模型相当，而无需访问急需的标签。在一组不同的基准异常值检测数据集上，AutoOD 始终优于从数百个检测器中选出的最佳无监督异常值检测器。它的 F-1 分数也优于其他免调优方法，从 12 到 97 分（满分 100 分）。
Outlier detection is critical in real world. Due to the existence of many outlier detection techniques which often return different results for the same data set, the users have to address the problem of determining which among these techniques is the best suited for their task and tune its parameters. This is particularly challenging in the unsupervised setting, where no labels are available for cross-validation needed for such method and parameter optimization. In this work, we propose AutoOD which uses the existing unsupervised detection techniques to automatically produce high quality outliers without any human tuning. AutoOD's fundamentally new strategy unifies the merits of unsupervised outlier detection and supervised classification within one integrated solution. It automatically tests a diverse set of unsupervised outlier detectors on a target data set, extracts useful signals from their combined detection results to reliably capture key differences between outliers and inliers. It then uses these signals to produce a "custom outlier classifier" to classify outliers, with its accuracy comparable to supervised outlier classification models trained with ground truth labels - without having access to the much needed labels. On a diverse set of benchmark outlier detection datasets, AutoOD consistently outperforms the best unsupervised outlier detector selected from hundreds of detectors. It also outperforms other tuning-free approaches from 12 to 97 points (out of 100) in the F-1 score.