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
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
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)
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)
Penar, W., Wozniak, M.: Cost sensitive methods of constructing hierarchical classifiers. Expert Systems 27(3), 146–155 (2010)
Kołakowska, A., Malina, W.: Fisher Sequential Classifiers. IEEE Transactions on Systems, Man and Cybernetics, Part B 35(5), 988–998 (2005)
Kurzyński, M.: On the Multistage Bayes Classifier. Pattern Recognition 21, 355–365 (1988)
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)
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)
Kurzyński, M.: Diagnosis of acute abdominal pain using three-stage classifier. Computers in Biology and Medicine 17(1), 19–27 (1987)
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)
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)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, London (1982)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons (2000)
Burduk, R., Kurzyński, M.: Two-stage binary classifier with fuzzy-valued loss function. Pattern Analysis and Applications 9(4), 353–358 (2006)
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
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Burduk, R., Zmyślony, M. (2012). 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, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-28931-6_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28930-9
Online ISBN: 978-3-642-28931-6
eBook Packages: Computer ScienceComputer Science (R0)