Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5778263 | Journal of Applied Logic | 2017 | 25 Pages |
Abstract
Over the years many anomaly and outlier metrics have been developed. In this paper we propose a clustering-based score ensembling method for outlier detection. Using benchmark datasets we evaluate quantitatively the robustness and accuracy of different ensemble strategies. We find that ensembling strategies offer only limited value for increasing overall performance, but provide robustness by negating the influence of severely underperforming models.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Logic
Authors
Alvin Chiang, Esther David, Yuh-Jye Lee, Guy Leshem, Yi-Ren Yeh,