Advertisement

Extraction of the Reduced Training Set Based on Rough Set in SVMs

  • Hongbing Liu
  • Shengwu Xiong
  • Qiong Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5552)

Abstract

In SVMs, the data points lying in the interactive regions of two classes are very important to form the hyperplane and difficult to be classified. How to select the reduced training set only including the interactive data points is one of the important issues. There are many methods by which the easy misclassified training data are selected to speed up training. The extraction method of the reduced training set is proposed by using the boundary of rough set. Firstly, for two-class problem, the entire training set is partitioned into three regions: the region only containing the positive samples, the region only composed of the negative samples and the boundary region including not only the positive samples but also the negative ones. Secondly, the boundary region is the intersection of two classes and selected to train SVMs. Thirdly, the two-class and multi-class problems are used to verify the feasibility of the proposed SVMs. The experimental results on the classic benchmark data set of machine learning show that the proposed learning machines can downsize the number of training data and hardly influence on their generalization abilities.

Keywords

SVMs Reduced training set Rough set Boundary 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Science 11, 341–356 (1982)CrossRefzbMATHGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publisher, Boston (1991)Google Scholar
  3. 3.
    Pawlak, Z.: Drawing Conclusions From Data—the Rough Set Way. International Journal of Intelligent Systems 16(1), 3–11 (2001)CrossRefzbMATHGoogle Scholar
  4. 4.
    Wei, J.M.: Rough Set Based Approach to Selection of Node. International Journal of Computational Cognition 1(2), 25–40 (2003)Google Scholar
  5. 5.
    Thangavel, K., Pethalakshmi, A.: Feature Selection for Medical Database Using Rough System. International Journal on Artificial Intelligence and Machine Learning 6(1), 11–17 (2005)Google Scholar
  6. 6.
    Wang, F.H.: On Acquiring Classification Knowledge From Noisy Data Based on Rough Set. Expert Systems with Applications 29(1), 49–64 (2005)CrossRefGoogle Scholar
  7. 7.
    Yu, C., Wu, M.H., Wu, M.: Combining Rough Set Theory with Neural Network Theory for Pattern Recognition. Neural Processing Letters 19(1), 73–87 (2004)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, New York (1998)zbMATHGoogle Scholar
  9. 9.
    Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Heidelberg (2005)Google Scholar
  10. 10.
    Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)CrossRefGoogle Scholar
  11. 11.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAG’s for Multi-class Classification, Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)Google Scholar
  12. 12.
    Abe, S., Inoue, T.: Fuzzy Support Vector Machines for Multiclass Problems. In: Proceedings of Tenth European Symposium on Artificial Neural Networks Conference, pp. 116–118 (2002)Google Scholar
  13. 13.
    Huang, H., Liu, Y.: Fuzzy Support Vector Machines for Pattern Recognition and Data Mining. International Journal of Fuzzy Systems 4(3), 826–835 (2002)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hongbing Liu
    • 1
    • 2
  • Shengwu Xiong
    • 1
  • Qiong Chen
    • 1
  1. 1.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  2. 2.Department of Computer ScienceXinyang Normal UniversityXinyangChina

Personalised recommendations