On the Evaluation of Outlier Detection and One-Class Classification Methods

Lorne Swersky, Henrique O. Marques, Jörg Sander, Ricardo J. G. B. Campello, Arthur Zimek

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 oneclass 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.
Original languageEnglish
Title of host publication3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016
EditorsRandall Bilof
Volume2016
PublisherIEEE Press
Publication date2016
Pages1-10
ISBN (Print)978-1-5090-5207-3
ISBN (Electronic)978-1-5090-5206-6
DOIs
Publication statusPublished - 2016
Event3rd IEEE International Conference on Data Science and Advanced Analytics - Montreal, Canada
Duration: 17 Oct 201619 Oct 2016
Conference number: 3

Conference

Conference3rd IEEE International Conference on Data Science and Advanced Analytics
Number3
CountryCanada
CityMontreal
Period17/10/201619/10/2016

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Cite this

Swersky, L., Marques, H. O., Sander, J., Campello, R. J. G. B., & Zimek, A. (2016). On the Evaluation of Outlier Detection and One-Class Classification Methods. In R. Bilof (Ed.), 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 (Vol. 2016, pp. 1-10). IEEE Press. https://doi.org/10.1109/DSAA.2016.8
Swersky, Lorne ; Marques, Henrique O. ; Sander, Jörg ; Campello, Ricardo J. G. B. ; Zimek, Arthur. / On the Evaluation of Outlier Detection and One-Class Classification Methods. 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 . editor / Randall Bilof. Vol. 2016 IEEE Press, 2016. pp. 1-10
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title = "On the Evaluation of Outlier Detection and One-Class Classification Methods",
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 oneclass 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.",
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Swersky, L, Marques, HO, Sander, J, Campello, RJGB & Zimek, A 2016, On the Evaluation of Outlier Detection and One-Class Classification Methods. in R Bilof (ed.), 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 . vol. 2016, IEEE Press, pp. 1-10, 3rd IEEE International Conference on Data Science and Advanced Analytics , Montreal, Canada, 17/10/2016. https://doi.org/10.1109/DSAA.2016.8

On the Evaluation of Outlier Detection and One-Class Classification Methods. / Swersky, Lorne; Marques, Henrique O.; Sander, Jörg; Campello, Ricardo J. G. B.; Zimek, Arthur.

3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 . ed. / Randall Bilof. Vol. 2016 IEEE Press, 2016. p. 1-10.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - 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 oneclass 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.

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Swersky L, Marques HO, Sander J, Campello RJGB, Zimek A. On the Evaluation of Outlier Detection and One-Class Classification Methods. In Bilof R, editor, 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 . Vol. 2016. IEEE Press. 2016. p. 1-10 https://doi.org/10.1109/DSAA.2016.8