Abstract
The following article describes an algorithm for constructing a decision tree classifier. It is a modified version of an algorithm in which one class was separated in every node of the tree. Now classes are divided into groups in every node. The proposed splitting method is based on Fisher criterion. The results of experiments on two real datasets are presented in comparison to the previous version of the algorithm.
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References
W.Malina: Two-Parameter Fisher Criterion. IEEE Trans. Systems, Man and Cybernetics, Vol. 31, No. 4, August 2001.
A. Kolakowska, W. Malina: Fisher sequential classifier. 5`h Conference Neural Networks and Soft Computing, Zakopane 6–10 czerwca 2000, str.227–232.
D. Mitchie, D. J. Spiegelhalter, C. C. Taylor: Machine Learning, Neural and Statistical Classification. 1994.
Safavian S.R., Landgrebe D.: A Survey of Decision Tree Classifier Methodology. IEEE Trans. Systems, Man, and Cybernet., Vol. 21, No. 3, May/June 1991.
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© 2003 Springer-Verlag Berlin Heidelberg
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Kołakowska, A., Malina, W. (2003). Sequential Classification. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_65
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_65
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
Online ISBN: 978-3-7908-1902-1
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