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

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Part of the book series: Advances in Intelligent Systems and Computing ((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.

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References

  1. Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1993)

    Google Scholar 

  2. Burduk, R.: Classification error in Bayes multistage recognition task with fuzzy observations. Pattern Analysis and Applications 13(1), 85–91 (2010)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  4. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2000)

    Google Scholar 

  5. Kurzyński, M.: Decision Rules for a Hierarchical Classifier. Pat. Rec. Let. 1, 305–310 (1983)

    Article  MATH  Google Scholar 

  6. Kurzyński, M.: Diagnosis of acute abdominal pain using three-stage classifier. Computers in Biology and Medicine 17(1), 19–27 (1987)

    Article  Google Scholar 

  7. Kurzyński, M.: On the Multistage Bayes Classifier. Pattern Recognition 21, 355–365 (1988)

    Article  MATH  Google Scholar 

  8. Manwani, N., Sastry, P.S.: Geometric decision tree. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(1), 181–192 (2012)

    Article  Google Scholar 

  9. Mitchell, T.M.: Machine Learning. McGraw-Hill Comp., Inc., New York (1997)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  12. Quinlan, J.R.: Induction on Decision Tree. Machine Learning 1, 81–106 (1986)

    Google Scholar 

  13. Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber. 21(3), 660–674 (1991)

    Article  MathSciNet  Google Scholar 

  14. Getting Started with SAS Enterprise Miner 6.1, http://support.sas.com/documentation/onlinedoc/miner

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© 2013 Springer International Publishing Switzerland

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Burduk, R. (2013). The Method of Improving the Structure of the Decision Tree Given by the Experts. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-00969-8_16

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00968-1

  • Online ISBN: 978-3-319-00969-8

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