On the evaluation of outlier detection and one-class classification methods

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.

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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: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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