Boosting Cost-Sensitive Trees

  • Kai Ming Ting
  • Zijian Zheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1532)

Abstract

This paper explores two techniques for boosting costsensitive trees. The two techniques differ in whether the misclassification cost information is utilized during training. We demonstrate that each of these techniques is good at different aspects of cost-sensitive classifications. We also show that both techniques provide a means to overcome the weaknesses of their base cost-sensitive tree induction algorithm

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Breiman, L. (1996), Bias, variance, and arcing classifiers, Technical Report 460, Department of Statistics, University of California, Berkeley, CA.Google Scholar
  2. 2.
    Breiman, L., J.H. Friedman, R.A. Olshen, & C.J. Stone (1984), Classification And Regression Trees, Belmont, CA: Wadsworth.MATHGoogle Scholar
  3. 3.
    Freund, Y.& R.E. Schapire (1996), Experiments with a new boosting algorithm, in Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann.Google Scholar
  4. 4.
    Knoll, U., G. Nakhaeizadeh, & B. Tausend (1994), Cost-sensitive pruning of decision trees, in Proceedings of the Eighth European Conference on Machine Learning, pp. 383–386. Berlin, Germany: Springer-Verlag.Google Scholar
  5. 5.
    Merz, C.J. & P.M. Murphy (1997), UCI Repository of machine learning databases [http://www.ics.uci.edu/mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.Google Scholar
  6. 6.
    Michie, D., D.J. Spiegelhalter, & C.C. Taylor (1994), Machine Learning, Neural and Statistical Classification, Ellis Horwood Limited.Google Scholar
  7. 7.
    Norton, S.W. (1989), Generating better decision trees, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 800–805, Morgan Kaufmann.Google Scholar
  8. 8.
    Núñez, M. (1991), The use of background knowledge in decision tree induction, Machine Learning, 6, pp. 231–250.Google Scholar
  9. 9.
    Pazzani, M., C. Merz, P. Murphy, K. Ali, T. Hume, & C. Brunk(1994), Reducing misclassification costs, in Proceedings of the Eleventh International Conference on Machine Learning, pp. 217–225, Morgan Kaufmann.Google Scholar
  10. 10.
    Quinlan, J.R. (1993), C4.5: Program for Machine Learning, Morgan Kaufmann.Google Scholar
  11. 11.
    Quinlan, J.R. (1996), Bagging, boosting, and C4.5, in Proceedings of the 13th National Conference on Artificial Intelligence, pp. 725–730, AAAI Press.Google Scholar
  12. 12.
    Schapire, R.E., Y. Freund, P. Bartlett, & W.S. Lee (1997), Boosting the margin: A new explanation for the effectiveness of voting methods, in Proceedings of the Fourteenth International Conference on Machine Learning, pp. 322–330. Morgan Kaufmann.Google Scholar
  13. 13.
    Tan, M. (1993), Cost-sensitive learning of classification knowledge and its applications in robotics, Machine Learning, 13, pp. 7–33.Google Scholar
  14. 14.
    Ting, K.M. (1998), Inducing cost-sensitive trees via instance-weighting, to appear in Proceedings of The Second European Symposium on Principles of Data Mining and Knowledge Discovery, Springer-Verlag.Google Scholar
  15. 15.
    Ting, K.M. & Z. Zheng (1998), Boosting trees for cost-sensitive classifications, Proceedings of the Tenth European Conference on Machine Learning, LNAI-1398, pp. 190–195, Berlin: Springer-Verlag.Google Scholar
  16. 16.
    Turney, P.D. (1995), Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 2, pp. 369–409.Google Scholar
  17. 17.
    Webb, G.I. (1996), Cost-sensitive specialization, in Proceedings of the 1996 Pacific Rim International Conference on Artificial Intelligence, pp. 23–34, Springer-Verlag.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Kai Ming Ting
    • 1
  • Zijian Zheng
    • 1
  1. 1.School of Computing and MathematicsDeakin UniversityVicAustralia

Personalised recommendations