Abstract
In support vector data description (SVDD) a spherically shaped boundary around a normal data set is used to separate this set from abnormal data. The volume of this data description is minimized to reduce the chance of accepting abnormal data. However the SVDD does not guarantee that the single spherically shaped boundary can best describe the normal data set if there are some distinctive data distributions in this set. A better description is the use of multiple spheres, however there is currently no investigation available. In this paper, we propose a theoretical framework to multi-sphere SVDD in which an optimisation problem and an iterative algorithm are proposed to determine model parameters for multi-sphere SVDD to provide a better data description to the normal data set. We prove that the classification error will be reduced after each iteration in this learning process. Experimental results on 28 well-known data sets show that the proposed multi-sphere SVDD provides lower classification error rate comparing with the standard single-sphere SVDD.
Original language | English |
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Title of host publication | Neural Information Processing. Models and Applications |
Subtitle of host publication | 17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part II |
Editors | Kok Wai Wong, B. Sumudu, U. Mendis, Abdesselam Bouzerdoum |
Place of Publication | Berlin Germany |
Publisher | Springer |
Pages | 132-142 |
Number of pages | 11 |
ISBN (Print) | 3642175333, 9783642175336 |
DOIs | |
Publication status | Published - 2010 |
Externally published | Yes |
Event | International Conference on Neural Information Processing 2010 - Sydney, Australia Duration: 22 Nov 2010 → 25 Nov 2010 Conference number: 17th https://link.springer.com/book/10.1007/978-3-642-17537-4 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 6444 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Neural Information Processing 2010 |
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Abbreviated title | ICONIP 2010 |
Country/Territory | Australia |
City | Sydney |
Period | 22/11/10 → 25/11/10 |
Internet address |
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Keywords
- imbalanced data
- novelty detection
- one-class classification
- spherically shaped boundary
- Support vector data description