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An experimental evaluation of novelty detection methods

Ding, Xuemei, Li, Yuhua, Belatreche, Ammar and Maguire, LP (2014) An experimental evaluation of novelty detection methods. Neurocomputing, 135 . pp. 313-327. [Journal article]

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URL: http://dx.doi.org/10.1016/j.neucom.2013.12.002

DOI: 10.1016/j.neucom.2013.12.002

Abstract

Novelty detection is especially important for monitoring safety-critical systems in which novel conditions rarely occur and knowledge about novelty in that system is often limited or unavailable. There are a large number of studies in the area of novelty detection, but there is a lack of a comprehensive experimental evaluation of existing novelty detection methods. This paper aims to fill this void by conducting experimental evaluation of representative novelty detection methods. It presents a state-of-the-art review of novelty detection, with a focus on methods reported in the last few years. In addition, a rigorous comparative evaluation of four widely used methods, representative of different categories of novelty detectors, is carried out using 10 benchmark datasets with different scale, dimensionality and problem complexity. The experimental results demonstrate that the k-NN novelty detection method exhibits competitive overall performance to the other methods in terms of the AUC metric.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:29187
Deposited By: Dr Yuhua Li
Deposited On:04 Apr 2014 10:05
Last Modified:17 Oct 2017 16:14

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