Advertisement

The Method of Improving the Structure of the Decision Tree Given by the Experts

  • Robert BurdukEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)

Abstract

This paper presents the problem of sequential decision making in the pattern recognition task. This task can be presented using a decision tree. In this case, it is assumed that the structure of the decision tree is determined by experts. The classification process is made in each node of the tree. This paper proposes a way to change the structure of the decision tree to improve the quality of classification. The split criterion is based on the confusion matrix. The obtained results were verified on the basis of the example of the computer-aided medical diagnosis.

Keywords

Decision Tree Internal Node Confusion Matrix Acute Abdominal Pain Neutral Network 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1993)Google Scholar
  2. 2.
    Burduk, R.: Classification error in Bayes multistage recognition task with fuzzy observations. Pattern Analysis and Applications 13(1), 85–91 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Burduk, R., Woźniak, M.: Different decision tree induction strategies for a medical decision problem. Central European Journal of Medicine 7(2), 183–193 (2010)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2000)Google Scholar
  5. 5.
    Kurzyński, M.: Decision Rules for a Hierarchical Classifier. Pat. Rec. Let. 1, 305–310 (1983)zbMATHCrossRefGoogle Scholar
  6. 6.
    Kurzyński, M.: Diagnosis of acute abdominal pain using three-stage classifier. Computers in Biology and Medicine 17(1), 19–27 (1987)CrossRefGoogle Scholar
  7. 7.
    Kurzyński, M.: On the Multistage Bayes Classifier. Pattern Recognition 21, 355–365 (1988)zbMATHCrossRefGoogle Scholar
  8. 8.
    Manwani, N., Sastry, P.S.: Geometric decision tree. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(1), 181–192 (2012)CrossRefGoogle Scholar
  9. 9.
    Mitchell, T.M.: Machine Learning. McGraw-Hill Comp., Inc., New York (1997)Google Scholar
  10. 10.
    Mui, J., Fu, K.S.: Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2, 429–443 (1980)Google Scholar
  11. 11.
    Penar, W., Woźniak, M.: Experiments on classifiers obtained via decision tree induction methods with different attribute acquisition cost limit. In: Kurzynski, M., et al. (eds.) Computer Recognition Systems. ASC, vol. 45, pp. 371–377. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Quinlan, J.R.: Induction on Decision Tree. Machine Learning 1, 81–106 (1986)Google Scholar
  13. 13.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Getting Started with SAS Enterprise Miner 6.1, http://support.sas.com/documentation/onlinedoc/miner

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations