FOOR: Be Careful for Outlier-Score Outliers When Using Unsupervised Outlier Ensembles

Abstract

Outlier detection is a very important tool in analyzing patterns and detecting unexpected events in social systems. However, the process of outlier detection could be fraught with uncertainty, with difficulties in determining the veracity of an object’s outlier score. We propose a framework for outlier-score outlier removal (FOOR). FOOR is a selection method, which aims to remove inaccurate outlier scores prior to data processing by ensemble techniques, to improve the accuracy of all ensembles. FOOR has rigorously tested with 30 real-world datasets and seven state-of-the-art ensembles over 25 different base detectors. Simulated experiments showed that FOOR significantly improves the existing techniques, with an average (AVG) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$+$</tex-math> </inline-formula> 0.05 AUC (from 0.81 to 0.86 AUC). Thus, we recommend FOOR as the new standard for outlier-score preprocessing before ensembles.