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A weighted one-class support vector machine
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DOI：10.1016/j.neucom.2015.10.097.png)
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摘要
En 中文
The standard one-class support vector machine (OC-SVM) is sensitive to noises, since every instance is equally treated. To address this problem, the weighted one-class support vector machine (WOC-SVM) was presented. WOC-SVM weakens the impact of noises by assigning lower weights. In this paper, a novel instance-weighted strategy is proposed for WOC-SVM. The weight is only relevant to the neighbors' distribution knowledge, which is only decided by k-nearest neighbors. The closer to the boundary of the data distribution the instance is, the lower the corresponding weight is. The experimental results demonstrate that WOC-SVM outperforms the standard OC-SVM when using the proposed instance weighted strategy. The proposed instance-weighted method performs better than previous ones. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Weighted one-class support vector machine
One-class classification
Neighbors' distribution knowledge
Instance weights
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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Boundary detection and sample reduction for one-class Support Vector Machines.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
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