An experimental evaluation of novelty detection methods

Ding, Xuemei, Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, Belatreche, Ammar and Maguire, Liam P. 2014. An experimental evaluation of novelty detection methods. Neurocomputing 135 , pp. 313-327. 10.1016/j.neucom.2013.12.002

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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:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
ISSN:	0925-2312
Date of Acceptance:	20 December 2013
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129140

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