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

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

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.
OriginalsprogEngelsk
Titel3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016
RedaktørerRandall Bilof
Vol/bind2016
ForlagIEEE Press
Publikationsdato2016
Sider1-10
ISBN (Trykt)978-1-5090-5207-3
ISBN (Elektronisk)978-1-5090-5206-6
DOI
StatusUdgivet - 2016
Begivenhed3rd IEEE International Conference on Data Science and Advanced Analytics - Montreal, Canada
Varighed: 17. okt. 201619. okt. 2016
Konferencens nummer: 3

Konference

Konference3rd IEEE International Conference on Data Science and Advanced Analytics
Nummer3
LandCanada
ByMontreal
Periode17/10/201619/10/2016

Fingeraftryk

Experiments

Citer dette

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. I R. Bilof (red.), 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 (Bind 2016, s. 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 . red. / Randall Bilof. Bind 2016 IEEE Press, 2016. s. 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.",
author = "Lorne Swersky and Marques, {Henrique O.} and J{\"o}rg Sander and Campello, {Ricardo J. G. B.} and Arthur Zimek",
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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. i R Bilof (red.), 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 . bind 2016, IEEE Press, s. 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 . red. / Randall Bilof. Bind 2016 IEEE Press, 2016. s. 1-10.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer 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. I Bilof R, red., 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 . Bind 2016. IEEE Press. 2016. s. 1-10 https://doi.org/10.1109/DSAA.2016.8