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适用于不平衡样本数据处理的支持向量机方法
引用本文:吴洪兴,彭宇,彭喜元.适用于不平衡样本数据处理的支持向量机方法[J].电子学报,2006,34(B12):2395-2398.
作者姓名:吴洪兴  彭宇  彭喜元
作者单位:哈尔滨工业大学自动化测试与控制系,黑龙江哈尔滨150080
摘    要:支持向量机算法在处理不平衡样本数据时,其分类器预测具有倾向性.样本数量多的类别,其分类误差小,而样本数量少的类别,其分类误差大.本文针对这种倾向性问题,在分析其产生原因的基础上,提出了基于遗传交叉运算的改进方法.对于小类别训练样本,利用交叉运算产生新的样本,从而补偿了因训练数据类别大小差异而造成的影响.基于UCI标准数据集的仿真实验结果表明,改进方法比标准支持向量机方法具有更好的分类准确率.

关 键 词:支持向量机  交叉算子  类别差异  模式识别
文章编号:0372-2112(2006)12A-2395-04
收稿时间:2006-08-22
修稿时间:2006-08-222006-11-13

A New Support Vector Machine Method for Unbalanced Data Treatment
WU Hong-xing, PENG Yu, PENG Xi-yuan.A New Support Vector Machine Method for Unbalanced Data Treatment[J].Acta Electronica Sinica,2006,34(B12):2395-2398.
Authors:WU Hong-xing  PENG Yu  PENG Xi-yuan
Affiliation:Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, Heilonsfiang 150080, China
Abstract:In SVM algorithm, when training sets with uneven class sizes are used, the prediction result of classifier is undesirably biased towards the class with more samples in the training set. That is to say, the larger the sample size, the smaller the classification error,whereas the smaller the sample size,the larger the classification error.Aiming at this orientation problem and with the analysis of the cause of it, an improved method based on genetic crossover operator was proposed, for the training set with small size generate new samples by using crossover operation, thereby compensates for the unfavorable impact caused by the bias of the training data class size. Simulation experiment results on UCI stander data shows that the proposed method has better classification accurate compared with stander support vector machine method.
Keywords:
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