Title :
A Weighted Support Vector Data Description Based on Rough Neighborhood Approximation
Author :
Yanxing Hu ; Liu, Jame N. K. ; Yuan Wang ; Lai, L.
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China
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.
Keywords :
approximation theory; pattern classification; rough set theory; support vector machines; element training set; rough neighborhood approximation; rough set theory; support vector algorithm; weighted support vector data description; Accuracy; Approximation methods; Kernel; Noise; Sensitivity; Support vector machines; Training; Neighborhood approximation; rough set; weighted SVDD;
Conference_Titel :
Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4673-5164-5
DOI :
10.1109/ICDMW.2012.124