Name
Abstract | ||
|---|---|---|
For a support vector algorithm, the problem of sensitivity to noise points is considered as one of the major problems that may affect the accuracy of the results. In this paper, a weighted method based on rough neighborhood approximation is proposed to reduce the influence of noise points for support vector data description algorithm, which is an important branch of support vector model. Based on the rough set theory, the element training set is divided into three regions, and the weight value is determined by the regions where a point is located. Experimental results showed that this proposed method can bring higher acceptance accuracy than that of classical support vector data description algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDMW.2012.124 | ICDM Workshops |
Keywords | Field | DocType |
noise point,description algorithm,support vector model,rough neighborhood approximation,support vector algorithm,support vector data description,element training set,vector data description,classical support vector data,weighted support,higher acceptance accuracy,support vector machines,approximation theory,rough set theory | Training set,Data mining,Computer science,Support vector machine,Approximation theory,Rough set,Artificial intelligence,Machine learning,Dominance-based rough set approach,Data description | Conference |
ISSN | Citations | PageRank |
2375-9232 | 4 | 0.39 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-Xing Hu | 1 | 79 | 5.74 |
| James N. K. Liu | 2 | 529 | 44.35 |
| Yuan Wang | 3 | 4 | 0.39 |
| Lucas Lai | 4 | 4 | 0.39 |