Advertisement

Effective Classifier Pruning with Rule Information

  • Xiaolong Zhang
  • Mingjian Luo
  • Daoying Pi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3735)

Abstract

This paper presents an algorithm to prune a tree classifier with a set of rules which are converted from a C4.5 classifier, where rule information is used as a pruning criterion. Rule information measures the goodness of a rule when discriminating labeled instances. Empirical results demonstrate that the proposed pruning algorithm has high predictive accuracy.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mehta, M., Rissanen, J., Agrawal, R.: MDL-Based Decision Tree Pruning. In: Proceedings of the First International Conference on KDD, pp. 216–221 (1995)Google Scholar
  2. 2.
    Breiman, L., et al.: Classification and Regression Trees. Wadsworth & Brooks Press (1984)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  4. 4.
    Hu, D., Li, H.X.: Rule Mining and Rule Reducing Based on the Information of Rules. Pattern Recognition and Artificial Intelligence 17(1) (2004)Google Scholar
  5. 5.
    Blake, C., Merz, C.: UCI Repository of Machine Learning Databases. Dept. of Information and Computer Science, University of CaliforniaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xiaolong Zhang
    • 1
    • 2
  • Mingjian Luo
    • 1
  • Daoying Pi
    • 2
  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanP.R. China
  2. 2.Dept. of Control Science and EngineeringZhejiang UniversityHangzhouP.R. China

Personalised recommendations