Abstract
| Original language | English |
|---|---|
| Title of host publication | 3rd IEEE International Conference on Data Science and Advanced Analytics : DSAA 2016 |
| Editors | Randall Bilof |
| Volume | 2016 |
| Publisher | IEEE Press |
| Publication date | 2016 |
| Pages | 1-10 |
| ISBN (Print) | 978-1-5090-5207-3 |
| ISBN (Electronic) | 978-1-5090-5206-6 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 3rd IEEE International Conference on Data Science and Advanced Analytics - Montreal, Canada Duration: 17. Oct 2016 → 19. Oct 2016 Conference number: 3 |
Conference
| Conference | 3rd IEEE International Conference on Data Science and Advanced Analytics |
|---|---|
| Number | 3 |
| Country | Canada |
| City | Montreal |
| Period | 17/10/2016 → 19/10/2016 |
Fingerprint
Cite this
}
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 proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - On the Evaluation of Outlier Detection and One-Class Classification Methods
AU - Swersky, Lorne
AU - Marques, Henrique O.
AU - Sander, Jörg
AU - Campello, Ricardo J. G. B.
AU - Zimek, Arthur
PY - 2016
Y1 - 2016
N2 - 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.
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.
U2 - 10.1109/DSAA.2016.8
DO - 10.1109/DSAA.2016.8
M3 - Article in proceedings
SN - 978-1-5090-5207-3
VL - 2016
SP - 1
EP - 10
BT - 3rd IEEE International Conference on Data Science and Advanced Analytics
A2 - Bilof, Randall
PB - IEEE Press
ER -