Advertisement

Decomposition of Classification Task with Selection of Classifiers on the Medical Diagnosis Example

  • Robert Burduk
  • Marcin Zmyślony
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7209)

Abstract

The article presents the concept of decomposition of the multidimensional classification task. The recognition procedure is divided into independent blocks. These blocks can be interpreted as lower classification problems. The structure of these blocks is presented as a decision tree. In this model the experts give the decision tree structure. The problem discussed in the work shows a selection of different classifiers (or their parameters) to the internal nodes of the decision tree. Experiments conducted for selected medical diagnosis problem show that the use of different classifiers can improve the quality of classification.

Keywords

Hierarchical classifier error probability ensemble classifiers 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mui, J., Fu, K.S.: Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans. Pattern Anal. PAMI-2, 429–443 (1980)Google Scholar
  2. 2.
    Wozniak, M.: Two-Stage Classifier for Diagnosis of Hypertension Type. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds.) ISBMDA 2006. LNCS (LNBI), vol. 4345, pp. 433–440. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Penar, W., Wozniak, M.: Cost sensitive methods of constructing hierarchical classifiers. Expert Systems 27(3), 146–155 (2010)CrossRefGoogle Scholar
  4. 4.
    Kołakowska, A., Malina, W.: Fisher Sequential Classifiers. IEEE Transactions on Systems, Man and Cybernetics, Part B 35(5), 988–998 (2005)CrossRefGoogle Scholar
  5. 5.
    Kurzyński, M.: On the Multistage Bayes Classifier. Pattern Recognition 21, 355–365 (1988)zbMATHCrossRefGoogle Scholar
  6. 6.
    De Dombal, F.T., Leaper, D.J., Staniland, J.R., McCann, A.P., Horrocks, C.: Computer-aided diagnosis of acute abdominal pain. Br. Med. J. II, 9–13 (1972)Google Scholar
  7. 7.
    Eich, H.P., Ohmann, C., Lang, K.: Decision support in acute abdominal pain using an expert system for different knowledge bases. In: Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems, pp. 2–7 (1997)Google Scholar
  8. 8.
    Kurzyński, M.: Diagnosis of acute abdominal pain using three-stage classifier. Computers in Biology and Medicine 17(1), 19–27 (1987)CrossRefGoogle Scholar
  9. 9.
    Burduk, R., Woźniak, M.: Bayes Multistage Classifier and Boosted C4.5 Algorithm in Acute Abdominal Pain Diagnosis. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 371–378. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Ohmann, C., Moustakis, V., Yang, Q., Lang, K.: Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain. Artif. Intell. Med. 8(1), 23–36 (1996)CrossRefGoogle Scholar
  11. 11.
    Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, London (1982)zbMATHGoogle Scholar
  12. 12.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2000)Google Scholar
  13. 13.
    Burduk, R., Kurzyński, M.: Two-stage binary classifier with fuzzy-valued loss function. Pattern Analysis and Applications 9(4), 353–358 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Systems, Man Cyber. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Getting Started with SAS Enterprise Miner 6.1, http://support.sas.com/documentation/onlinedoc/miner

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Robert Burduk
    • 1
  • Marcin Zmyślony
    • 1
  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

Personalised recommendations