A study on anomaly detection ensembles

Alvin Chiang, Esther David*, Yuh-Jye Lee, Guy Leshem, Yi Ren Yeh

*此作品的通信作者

研究成果: Article同行評審

19 引文 斯高帕斯(Scopus)

摘要

An anomaly, or outlier, is an object exhibiting differences that suggest it belongs to an as-yet undefined class or category. Early detection of anomalies often proves of great importance because they may correspond to events such as fraud, spam, or device malfunctions. By automating the creation of a ranking or list of deviations, we can save time and decrease the cognitive overload of the individuals or groups responsible for responding to such events. 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.

原文English
頁(從 - 到)1-13
頁數13
期刊Journal of Applied Logic
21
DOIs
出版狀態Published - 1 5月 2017

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