An Application of Binary Decision Trees to Pattern Recognition

Noboru Takagi

doi:10.20965/jaciii.2006.p0682

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JACIII Vol.10 No.5 pp. 682-687

doi: 10.20965/jaciii.2006.p0682

(2006)

Paper:

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An Application of Binary Decision Trees to Pattern Recognition

Noboru Takagi

Department of Intelligent Systems Design Engineering, Toyama Prefectural University, 5180 Kurokawa, Imizu, Toyama 939-0398, Japan

Received:

January 11, 2006

Accepted:

February 20, 2006

Published:

September 20, 2006

Keywords:

decision rule, off-line handwriting recognition, subclass method, pattern recognition

Abstract

Decision rules are a key technique in decision making, data mining and knowledge discovery in databases. We introduce an application of decision rules, handwriting pattern classification. When decision rules are applied to pattern recognition, one rule forms a hyperrectangle in feature space, i.e., each decision rule corresponds to one hyperrectangle. This means that a set of decision rules is considered a classification system, called the subclass method. We apply decision rules to handwritten Japanese character recognition, showing experimental results.

Cite this article as:

N. Takagi, “An Application of Binary Decision Trees to Pattern Recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.5, pp. 682-687, 2006.

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References

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[9] T. Nakajima, T. Wakabayashi, F. Kimura, and Y. Miyake, “Accuracy Improvement by Compound Discriminant Functions for Resembling Character Recognition,” IEICE Trans. on Information and Systems, D-II, Vol.J83-D-II, No.2, pp. 623-633, 2000.
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[13] http://www.is.aist.go.jp/etlcdb/

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] L. Breiman, J. H. Friedman, R. A. Olshen, and C. H. Stone, “Classification and Regression Trees,” Chapman & Hall, 1984.

[2] [2] Z. Pawlak, “Rough Sets –Theory and Applications–,” Kluwer Academic Publishers, Dordrecht, 1991.

[3] [3] J. R. Quinlan, “C4.5 Programs for Machine Learning,” Morgan Kaufmann Publishers, 1993.

[4] [4] U. M. Fayyad, “Advances in Knowledge Discovery and Data Mining,” MIT Press, 1996.

[5] [5] M. Kudo and M. Shimbo, “Optimal Subclasses with Dichotomous Variables for Feature Selection and Discrimination,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.19, No.5, pp. 1194-1199, 1989.

[6] [6] M. Kudo, Y. Torii, Y. Mori, and M. Shimbo, “Approximation of Class Regions by Quasi Convex Hulls,” Pattern Recognition Letter, Vol.19, pp. 777-786, 1998.

[7] [7] S. M. Weiss and C. A. Kulikowski, “Computer Systems That Learn – Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems,” Morgan Kaufman Publishers, Inc., 1991.

[8] [8] N. Sun, M. Abe, and Y. Nemoto, “A Handwritten Character Recognition System by Using Improved Directional Element Feature and Subspace Method,” IEICE Trans. on Information and Systems, DII, Vol.J78-D-II, No.6, pp. 922-930, 1995.

[9] [9] T. Nakajima, T. Wakabayashi, F. Kimura, and Y. Miyake, “Accuracy Improvement by Compound Discriminant Functions for Resembling Character Recognition,” IEICE Trans. on Information and Systems, D-II, Vol.J83-D-II, No.2, pp. 623-633, 2000.

[10] [10] M. Suzuki, N. Kato, Y. Nemoto, and H. Ichimura, “A Discriminant Method of Similar Characters with Quadratic Compound Function,” IEICE Trans. on Information and Systems, D-II, Vol.J84-D-II, No.8, pp. 1557-1565, 2001.

[11] [11] J. W. Grzymala-Busse, “LERS – A System for Learning From Examples Based on Rough Sets,” Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, pp. 3-18, 1992.

[12] [12] J. G. Bazan and M. Szczuka, “RSES and RSESlib – A Collection of Tools for Rough Set Computations,” Proc. of Rough Sets and Current Trends in Computing 2000, Lecture Note in Artificial Intelligence 2005, Springer-Verlag, pp. 106-113, 2001.

[13] [13] http://www.is.aist.go.jp/etlcdb/