Abstract
Current well-known data description methods such as Support Vector Data Description and Small Sphere Large Margin are conducted with assumption that data samples of a class in feature space are drawn from a single distribution. Based on this assumption, a single hypersphere is constructed to provide a good data description for the data. However, real-world data samples may be drawn from some distinctive distributions and hence it does not guarantee that a single hypersphere can offer the best data description. In this paper, we introduce a Fuzzy Multi-sphere Support Vector Data Description approach to address this issue. We propose to use a set of hyperspheres to provide a better data description for a given data set. Calculations for determining optimal hyperspheres and experimental results for applying this proposed approach to classification problems are presented.
Original language | English |
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Title of host publication | Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings |
Publisher | Springer |
Pages | 570-581 |
Number of pages | 12 |
Edition | PART 2 |
ISBN (Print) | 9783642374555 |
DOIs | |
Publication status | Published - 1 Dec 2013 |
Externally published | Yes |
Event | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013 - Gold Coast, Australia Duration: 14 Apr 2013 → 17 Apr 2013 Conference number: 17th https://link.springer.com/book/10.1007/978-3-642-37453-1 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Number | PART 2 |
Volume | 7819 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Pacific-Asia Conference on Knowledge Discovery and Data Mining 2013 |
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Abbreviated title | PAKDD 2013 |
Country/Territory | Australia |
City | Gold Coast |
Period | 14/04/13 → 17/04/13 |
Internet address |
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Keywords
- Fuzzy interference
- Kernel methods
- Multi-Sphere support vector data description
- Support vector data description