Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2015.10.097 | Neurocomputing |
Keywords | Field | DocType |
Weighted one-class support vector machine,One-class classification,Neighbors׳ distribution knowledge,Instance weights | Structured support vector machine,One-class classification,Pattern recognition,Support vector machine,Artificial intelligence,Relevance vector machine,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
189 | C | 0925-2312 |
Citations | PageRank | References |
10 | 0.56 | 19 |
Authors | ||
6 | ||