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A weighted one-class support vector machine
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AI
DOI：10.1016/j.neucom.2015.10.097.png)
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Abstract
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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.
Keywords:
Weighted one-class support vector machine
One-class classification
Neighbors' distribution knowledge
Instance weights
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6.5
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2.5W
Citations:
6.5W
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Cited Papers
ShareSaveBoundary detection and sample reduction for one-class Support Vector Machines.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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