论文标题
简单的邻里代表性预处理提升探测器
Simple Neighborhood Representative Pre-processing Boosts Outlier Detectors
论文作者
论文摘要
在过去的几十年中，传统的离群探测器通过仅评估对象级因子来计算数据中的对象的离群值分数在计算群体级别时忽略了群体级别的因子，但未能捕获集体离群值。为了减轻此问题，我们提出了一种称为邻里代表（NR）的方法，该方法使所有现有的异常检测器赋予了有效检测异常值，包括集体异常值，同时保持其计算完整性。它通过选择代表对象，评分这些对象，然后将代表对象的分数应用于其集体对象来实现这一目标。在不更改现有检测器的情况下，NR与现有检测器兼容，同时相对于最先进的离群检测器，在 +8％（0.72至0.78 AUC）的现实世界数据集上提高了性能。
Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers. To mitigate this issue, we present a method called neighborhood representative (NR), which empowers all the existing outlier detectors to efficiently detect outliers, including collective outliers, while maintaining their computational integrity. It achieves this by selecting representative objects, scoring these objects, then applies the score of the representative objects to its collective objects. Without altering existing detectors, NR is compatible with existing detectors, while improving performance on real world datasets with +8% (0.72 to 0.78 AUC) relative to state-of-the-art outlier detectors.
