期刊文献+

基于支持向量机的不平衡数据分类算法的研究 被引量:8

Imbalance data classification algorithm based on support vector machines
下载PDF
导出
摘要 针对不平衡数据分类问题,提出了基于Smote与核函数修改相结合的算法。首先用Smote方法处理数据,降低不平衡度;然后以黎曼几何为依据,利用保角变换,对核函数进行修改,提高支持向量机的分类泛化能力;最后用修改后的支持向量机对新的数据进行处理。实验结果表明,这种方法能在保持整体正确率的前提下有效地提高少数类样本的分类准确率。 In view of the classification of the imbalance date set, this paper gave the method using SMOTE and modify kernel. First, used SMOTE method processing data, to reduce the imbalance. Then, used the conformal transformation and Riemannian metric to modify kernel, and reconstructed a new SVM with the modified kernel. Finally, used the new SVM to process the new data. Experimental results show that this method can improve the accuracy of the class with less training data under a high total accuracy.
出处 《计算机应用研究》 CSCD 北大核心 2009年第8期2874-2875,2901,共3页 Application Research of Computers
基金 国家青年科学基金资助项目(60805014)
关键词 SMOTE 黎曼几何 核函数 支持向量机 Smote Riemannian geometry kernel function support vector machines
  • 相关文献

参考文献5

二级参考文献85

  • 1V N Vapnik. The Nature of Statistical Learning Theory [M]. Second Edition, New York :Springer, 2000.
  • 2陈维桓,李兴校.黎曼几何引论[M].北京:北京大学出版社,2002.
  • 3S Amari, S Wu. Improving support vector machine classifiers by modifying kernel functions[J]. Neural Networks(S0893-6080), 1999,12: 783-789.
  • 4S. Wu, S. Amari. Conformal transformation of kernel functions: a data-dependent way to improve support vector machine classifiers[J].Neural Processing Letters(S1370-4621), 2002, 15: 59-67.
  • 5C J C Burges. Geometry and invariance in kernel based methods[C]//B Scholkopf, C J C. Bulges, and A J Smola. Advances in kernel methods - Support Vector Learning. Cambridge, MA: MIT Press,1999.89-116.
  • 6O Chapelle, V N Vapnik et al. Choosing multiple parameters for support vector machines[J]. Machine Learning(S0885-6125), 2002,46: 131-159.
  • 7VAPNIK V N. The nature of statistical learning theory[M]. 2nd ed. New York: Springer-Verlag, 2000: 17-180.
  • 8TREVOR H, ROBERT T, JEROINE F. The Elements of Statistical Learning: Data Mining, Inference, and Prediction[M]. New York: Springer-Verlag, 2001: 1-8;371-406.
  • 9KEERTHI S S, LIN C J. Asymptotic behaviors of support vector machines with Gaussian kernel[J]. Neural Computation, 2003, 15(7): 1667-1689.
  • 10LIN H T, LIN C J. A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods [R]. Taipei: Department of Computer Science and Information Engineering, National Taiwan University. 2003.

共引文献92

同被引文献73

引证文献8

二级引证文献32

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部