Advertisement

Boosting trees for cost-sensitive classifications

  • Kai Ming Ting
  • Zijian Zheng
Multiple Models for Classification
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

This paper explores two boosting techniques for cost-sensitive tree classifications in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced model for different misclassification costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this change. We demonstrate that the two boosting techniques are a good solution in different aspects under this situation.

Keywords

Cost Matrix Cost Change Incorrect Prediction Misclassification Cost Robust Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Merz, C.J. & P.M. Murphy (1997), UCI Repository of machine learning databases [http://www.ics.uci.edu/≈mlearn/MLRepository.htmll. Irvine, CA: University of California, Department of Information and Computer Science.Google Scholar
  2. Michie, D., D.J. Spiegelhalter, & C.C. Taylor (1994), Machine Learning, Neural and Statistical Classification, Ellis Horwood Limited.Google Scholar
  3. 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
  4. Quinlan, J.R. (1993), C4.5: Program for machine learning, Morgan Kaufmann.Google Scholar
  5. 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
  6. 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.Google Scholar
  7. Ting, K.M. & Z. Zheng & Boosting Trees for Cost-Sensitive Classifications, Working Paper 1/98, Dept. of Computer Science, University of Waikato.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Kai Ming Ting
    • 1
  • Zijian Zheng
    • 2
  1. 1.Department of Computer ScienceUniversity of ZhengHamiltonNew Zealand
  2. 2.School of Computing and MathematicsDeakin UniversityAustralia

Personalised recommendations