不平衡支持向量机的平衡方法<sup>*</sup>

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Abstract An adjustment method is proposed for the separation hyperplane of binaryclassification 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, onedimensional data are generated by projecting the high dimensional data onto the normal vector. Then, the ratio of the twoclass penalty factors is determined based on the information derived from the standard deviation of the projective data and the twoclass 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.

Key words： Imbalanced Data Feature Extraction Support Vector Machines (SVM) Projection Standard Deviation

Received: 26 March 2007

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	LIU WanLi
	LIU SanYang
	XUE ZhenXia

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LIU WanLi,LIU SanYang,XUE ZhenXia. Balance Method for Imbalanced Support Vector Machines[J]. , 2008, 21(2): 136-141.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2008/V21/I2/136

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