Balance Method for Imbalanced Support Vector Machines
LIU WanLi1,2, LIU SanYang1, XUE ZhenXia1,3
1.Department of Applied Mathematics, Xidian University, Xi'an 7100712. Department of Mathematics, Luoyang Normal College, Luoyang 4710223. Department of Mathematics, Henan Science and Technology University, Luoyang 471003
Abstract An adjustment method is proposed for the separation hyperplane of binaryclassification imbalanced data. Firstly, the original samples are preliminarily trained by the standard support vector machines, and a normal vector of the separation hyperplane is obtained. Secondly, onedimensional data are generated by projecting the high dimensional data onto the normal vector. Then, the ratio of the twoclass penalty factors is determined based on the information derived from the standard deviation of the projective data and the twoclass sample sizes. Finally, a new separation hyperplane is presented by the second training. Experimental results show the efficiency, i.e., the two error ratios can be balanced and even be decreased generally.
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2008, Vol. 21 
