An experimental evaluation of novelty detection methods

Ding, X, Li, Y, Belatreche, A and Maguire, LP 2014, 'An experimental evaluation of novelty detection methods' , Neurocomputing, 135 , pp. 313-327.

PDF - Published Version Restricted to Repository staff only Download (1MB)

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
Themes:	Media, Digital Technology and the Creative Economy
Schools:	Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	Neurocomputing
Publisher:	Elsevier
Refereed:	Yes
ISSN:	0925-2312
Related URLs:	Publication
Funders:	Funder not known
Depositing User:	Yuhua Li
Date Deposited:	29 Jan 2015 12:26
Last Modified:	15 Feb 2022 15:47
URI:	https://usir.salford.ac.uk/id/eprint/33093

Actions (login required)

Edit record (repository staff only)