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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1993)
Burduk, R.: Classification error in Bayes multistage recognition task with fuzzy observations. Pattern Analysis and Applications 13(1), 85–91 (2010)
Burduk, R., Woźniak, M.: Different decision tree induction strategies for a medical decision problem. Central European Journal of Medicine 7(2), 183–193 (2010)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2000)
Kurzyński, M.: Decision Rules for a Hierarchical Classifier. Pat. Rec. Let. 1, 305–310 (1983)
Kurzyński, M.: Diagnosis of acute abdominal pain using three-stage classifier. Computers in Biology and Medicine 17(1), 19–27 (1987)
Kurzyński, M.: On the Multistage Bayes Classifier. Pattern Recognition 21, 355–365 (1988)
Manwani, N., Sastry, P.S.: Geometric decision tree. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(1), 181–192 (2012)
Mitchell, T.M.: Machine Learning. McGraw-Hill Comp., Inc., New York (1997)
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)
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)
Quinlan, J.R.: Induction on Decision Tree. Machine Learning 1, 81–106 (1986)
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber. 21(3), 660–674 (1991)
Getting Started with SAS Enterprise Miner 6.1, http://support.sas.com/documentation/onlinedoc/miner
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)