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SVM中不平衡数据的分离超平面的校正方法 被引量：3

Revising method for separation hyperplane of imbalanced data in SVM
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摘要 针对两类不平衡数据的分离超平面的偏移问题提出一种平衡方法。首先,对两类样本数据在核空间中进行核主成分分析,分别求出两类样本数据的在特征空间中的主要特征值;然后,根据两样本容量以及各自的特征值所提供的信息,对两类数据给出惩罚因子比例;最后,通过优化训练,产生一个新的分离超平面。该分类面校正了标准的支持向量机的分类误差。实验显示了所提出方法的有效性,即与标准的支持向量机相比,不仅平衡了错分率而且还能减少错分率。 A balance method for the offset of separation hyperplane of biclassification imbalanced data is proposed.Firstly,the principal eigenvalues are found respectively of the two classes of samples in feature space by using Kernel Principal Component Analysis（KPCA）.Secondly,one penalty proportion is given based on the information provided by the sizes of the two sample data and their eigenvalues.Finally,a new separation hyperplane is generated by the optimization training.The hyperplane revises the error of the standard Support Vector Machines.Experiments show the efficiency of proposed method,i.e.comparing with standard Support Vector Machines the classification error can be balanced and be also decreased.
作者 刘万里 刘三阳
机构地区 洛阳师范学院数学科学学院
出处 《计算机工程与应用》 CSCD 北大核心 2008年第19期169-171,共3页 Computer Engineering and Applications
基金 国家自然科学基金(the National Natural Science Foundation of China under Grant No.60574075 No.60674108)
关键词 不平衡数据 核主成分分析 支持向量机 偏移 imbalaneed data Kernel Principal Component Analysis（KPCA） Support Vector Machines（SVM） offset
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
作者简介 刘万里（1964-），男，副教授，博士生，研究方向为：机器学习、最优化方法及应用； E-mail ： lwanli@lynu.edu.cn 刘三阳（1959-），男，教授，博士生导师，研究方向为：最优化理论、方法及应用。
  • 相关文献

参考文献9

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二级参考文献21

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共引文献33

同被引文献37

  • 1龙晓林,蒋静坪.基于SVM的机器人工件识别[J].华中科技大学学报（自然科学版）,2005,33(2):41-43. 被引量：6
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  • 6Chawla N V,Hall L O,Bowyer K W,et al.SMOTE:Synthetic Minority Oversampling Technique[J].Journal of Articial Intelligence Research,2002,16(3):321-357.
  • 7He Haibo,Garcia E A.Learning from Imbalanced Data[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(9):1263-1284.
  • 8Zhou Zhihua,Liu Xuying.Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(1):63-77.
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  • 10Veropulos K,Campbell C,Cristianini N.Controlling the Sensitivity of Support Vector Machine[C]//Proc.of International Joint Conference on Artificial Intelligence.Stockholm,Sweden:[s.n.],1999:55-60.

引证文献3

二级引证文献18

计算机工程与应用
2008年 第19期

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