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2013 IEEE 13th International Conference on Data Mining Workshops (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 635-642
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.
Training, Noise, Support vector machines, Approximation methods, Accuracy, Sensitivity, Kernel, Neighborhood approximation, weighted SVDD, rough set
Yanxing Hu, James N.K. Liu, Yuan Wang, Lucas Lai, "A Weighted Support Vector Data Description Based on Rough Neighborhood Approximation", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 635-642, 2012, doi:10.1109/ICDMW.2012.124
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