  Outlier detection (OD) is a key machine learning (ML) task for identifying
abnormal objects from general samples with numerous high-stake applications
including fraud detection and intrusion detection. Due to the lack of ground
truth labels, practitioners often have to build a large number of unsupervised,
heterogeneous models (i.e., different algorithms with varying hyperparameters)
for further combination and analysis, rather than relying on a single model.
How to accelerate the training and scoring on new-coming samples by
outlyingness (referred as prediction throughout the paper) with a large number
of unsupervised, heterogeneous OD models? In this study, we propose a modular
acceleration system, called SUOD, to address it. The proposed system focuses on
three complementary acceleration aspects (data reduction for high-dimensional
data, approximation for costly models, and taskload imbalance optimization for
distributed environment), while maintaining performance accuracy. Extensive
experiments on more than 20 benchmark datasets demonstrate SUOD's effectiveness
in heterogeneous OD acceleration, along with a real-world deployment case on
fraudulent claim analysis at IQVIA, a leading healthcare firm. We open-source
SUOD for reproducibility and accessibility.
