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Construction of Sequential Classifier Using Confusion Matrix

  • Robert Burduk
  • Pawel Trajdos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8104)

Abstract

This paper presents the problem of building the decision scheme in the multistage pattern recognition task. This task can be presented using a decision tree. This decision tree is built in the learning phase of classification. This paper proposes a split criterion based on the analysis of the confusion matrix. Specifically, we propose the division associated with an incorrect classification. The obtained results were verified on the data sets form UCI Machine Learning Repository and one real-life data set of the computer-aided medical diagnosis.

Keywords

Multistage classifier sequential classifier confusion matrix 

References

  1. 1.
    Kołakowska, A., Malina, W.: Fisher Sequential Classifiers. IEEE Transaction on Systems, Man, and Cybernecics – Part B Cybernecics 35(5), 988–998 (2005)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Podolak, I.T.: Hierarchical classifier with overlapping class groups. Expert Syst. Appl. 34(1), 673–682 (2008)CrossRefGoogle Scholar
  4. 4.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Penar, W., Woźniak, M.: Experiments on classifiers obtained via decision tree induction methods with different attribute acquisition cost limit. Advances in Soft Computing 45, 371–377 (2007)CrossRefGoogle Scholar
  6. 6.
    Quinlan, J.R.: Induction on Decision Tree. Machine Learning 1, 81–106 (1986)Google Scholar
  7. 7.
    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
  8. 8.
    Kurzyński, M.: Decision Rules for a Hierarchical Classifier. Pat. Rec. Let. 1, 305–310 (1983)CrossRefzbMATHGoogle Scholar
  9. 9.
    Woźniak, M.: A hybrid decision tree training method using data streams. Knowledge and Information Systems 29(2), 335–347 (2010)CrossRefGoogle Scholar
  10. 10.
    Choraś, M.: Image feature extraction methods for ear biometrics–A survey. In: 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2007, pp. 261–265. IEEE (2007)Google Scholar
  11. 11.
    Choraś, R.S.: Content-based retrieval using color, texture, and shape information. In: Progress in Pattern Recognition, Speech and Image Analysis, pp. 619–626 (2003)Google Scholar
  12. 12.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  13. 13.
    Rejer, I.: Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) CORES 2013. AISC, vol. 226, pp. 579–589. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Burduk, R., Zmyślony, M.: Decomposition of classification task with selection of classifiers on the medical diagnosis example. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS, vol. 7209, pp. 569–577. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Burduk, R.: Classification error in Bayes multistage recognition task with fuzzy observations. Pattern Analysis and Applications 13(1), 85–91 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kurzyński, M.: On the Multistage Bayes Classifier. Pattern Recognition 21, 355–365 (1988)CrossRefzbMATHGoogle Scholar
  17. 17.
    Berger, J.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1993)Google Scholar
  18. 18.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2000)Google Scholar
  19. 19.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar
  20. 20.
    Burduk, R., Woźniak, M.: Different decision tree induction strategies for a medical decision problem. Central European Journal of Medicine 7(2), 183–193 (2010)Google Scholar
  21. 21.
    Kurzyński, M.: Diagnosis of acute abdominal pain using three-stage classifier. Computers in Biology and Medicine 17(1), 19–27 (1987)CrossRefGoogle Scholar
  22. 22.
    Trawiński, B., Smetek, M., Telec, Z., Lasota, T.: Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. International Journal of Applied Mathematics and Computer Science 22(4), 867–881 (2012)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Bobrowski, L., Topczewska, M.: Separable Linearization of Learning Sets by Ranked Layer of Radial Binary Classifiers. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) CORES 2013. AISC, vol. 226, pp. 131–140. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  25. 25.
    MacArthur, R.: On the relative abundance of bird species. Proc. Natl. Acad. Sci. USA 43, 293–295 (1957)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Robert Burduk
    • 1
  • Pawel Trajdos
    • 1
  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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