AAAI Publications, The Twenty-Ninth International Flairs Conference

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Adaptive Sampling and Learning for Unsupervised Outlier Detection
Zhiruo Zhao, Chilukuri Mohan, Kishan Mehrotra

Last modified: 2016-03-30


Unsupervised outlier detection algorithms often suffer from high false positive detection rates. Ensemble approaches can be used to address this problem. This paper proposes a novel ensemble method which adopts the use of an adaptive sampling approach, and combines the outputs of individual anomaly detection algorithms by a weighted majority voting rule in a complete unsupervised context. Simulations on well-known benchmark problems show substantial improvement in performance.


Anomaly Detection; Unsupervised Learning; Adaptive Sampling; Adaptive Learning; Weighted Majority Voting;

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