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 language | English |
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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 |
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Number | 3 |
Country/Territory | Canada |
City | Montreal |
Period | 17/10/2016 → 19/10/2016 |
Keywords
- Evaluation
- Machine learning algorithms
- One-class classification
- Outlier detection
- Predictive models
- Semi-supervised learning
- Unsupervised learning