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中文摘要：
针对支持向量机中两类不平衡数据的分离超平面提出一种调整算法．首先用标准的支持向量机对原始数据进行初步训练，产生一个分离超平面的法向量．然后把高维样本投影到该法向量上得到一维数据．最后由投影数据的标准差以及样本容量所提供的信息，给出两类数据惩罚因子比例，再用标准的支持向量机进行第2次训练，从而得到一个新的分离超平面．实验显示该方法的有效性，即在一般情况下能平衡错分率，甚至还能减少错分率．
英文摘要：
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.
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