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| Density-Punished Support Vector Data Description |
| ZHANG Li1.2, ZHANG Hai-Fei1, ZHOU Wei-Da3, LIN Ying2, LI Fan-Zhang1 |
1.School of Computer Science and Technology, Soochow University, Suzhou 215006
2.Institute of Intelligence Information Processing, Xidian University, Xi'an 710071
3.AI Speech Ltd., Suzhou 215123 |
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Abstract Based on the concept of relative density degrees, a density-punished support vector data description method is presented. The relative density degrees are associated with punishing misclassifications. If the relative density degree of the sample is large, it is likely to be a target sample. Thus, a large penalty should be put on its misclassification. Similarly, if the relative densitydegree of the sample is small, it might be a boundary or noise point so that the corresponding penalty for its misclassification should be small as well. The experimental results on UCI datasets show that the proposed method has better performance compared with support vector data description and density-induced support vector data description.
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Received: 13 May 2013
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2014, Vol. 27 
