@inproceedings{49476e758de345e88a82ba3531944060,
title = "Adaptive sampling and learning for unsupervised outlier detection",
abstract = "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.",
author = "Zhiruo Zhao and Mohan, {Chilukuri K.} and Mehrotra, {Kishan G}",
note = "Publisher Copyright: Copyright {\textcopyright} 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All right reserved.; 29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016 ; Conference date: 16-05-2016 Through 18-05-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of the 29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016",
publisher = "AAAI Press",
pages = "460--465",
editor = "Zdravko Markov and Ingrid Russell",
booktitle = "Proceedings of the 29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016",
}