Simple Neighborhood Representative Pre-processing Boosts Outlier Detectors
This addresses the issue of detecting collective outliers in data for applications using outlier detection, though it is incremental as it builds on existing methods without altering them.
The paper tackles the problem of traditional outlier detectors failing to capture collective outliers by introducing a neighborhood representative pre-processing method that boosts existing detectors, achieving an 8% relative improvement in AUC from 0.72 to 0.78 on real-world datasets.
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.