Improving SVM-Linear Predictions Using CART for Example Selection

  • João M. Moreira
  • Alípio M. Jorge
  • Carlos Soares
  • Jorge Freire de Sousa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


This paper describes the study on example selection in regression problems using μ-SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising.


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  1. 1.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97(1-2), 245–271 (1997)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Chapman and Hall/CRC (1984)Google Scholar
  3. 3.
    Cardie, C.: Using decision trees to improve case-based learning. In: 10th International conference on machine learning, pp. 25–32. Morgan Kaufmann, San Francisco (1993)Google Scholar
  4. 4.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)MATHCrossRefGoogle Scholar
  5. 5.
    Liu, H., Motoda, H.: On issues of instance selection. Data Mining and Knowledge Discovery 6(2), 115–130 (2002)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Moreira, J.M., Jorge, A., Sousa, J.F., Soares, C.: Trip time prediction in mass transit companies. a machine learning approach. In: 10th EWGT, pp. 276–283 (2005)Google Scholar
  7. 7.
    Scholkopf, B., Smola, A.J., Williamson, R., Bartlett, P.: New support vector algorithms. Technical Report NC2-TR-1998-031 (1998)Google Scholar
  8. 8.
    Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Technical Report NC2-TR-1998-030 (1998)Google Scholar
  9. 9.
    Syed, N.A., Liu, H., Sung, K.K.: A study of support vectors on model independent example selection. In: 5th ACM SIGKDD, pp. 272–276 (1999)Google Scholar
  10. 10.
    Team, R.D.C.: R: A language and environment for statistical computing. Technical report, R Foundation for Statistical Computing (2004)Google Scholar
  11. 11.
    Torgo, L.: Regression data repository,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • João M. Moreira
    • 1
  • Alípio M. Jorge
    • 2
  • Carlos Soares
    • 2
  • Jorge Freire de Sousa
    • 2
  1. 1.Faculty of EngineeringUniversity of PortoPortugal
  2. 2.Faculty of Economics, LIACCUniversity of PortoPortugal

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