Swersky, Lorne , Marques, Henrique O., Sander, Jörg, Campello, Ricardo J.G.B., and Zimek, Arthur (2016) On the evaluation of outlier detection and one-class classification methods. In: Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics. pp. 1-10. From: DSAA 2016: 3rd IEEE International Conference on Data Science, 17-19 October 2016, Montréal, Canada.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of one-class classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. Our experiments led to conclusions that do not fully agree with those of previous work.

Item ID:	47735
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-5090-5206-6
Keywords:	semi-supervised learning; unsupervised learning; one-class classification; outlier detection; machine learning algorithms; predictive models; evaluation
Funders:	FAPESP, CNPq, Brazil
Projects and Grants:	FAPESP 2015/06019-0, CNPq 304137/2013-8, CNPq 400772/2014-0
Date Deposited:	28 Mar 2017 23:48
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
Downloads:	Total: 2
	More Statistics