Abstract
A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.
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References
Bennett, K.P., Mangasarian, O.L.: Robust Linear Programming Discrimination of Two Linearly Inseparable Sets. Optimization Methods and Software 1, 23–34 (1992)
Bennett, K.P., Wu, D.H., Auslender, L.: On Support Vector Desision Trees for Database Marketing, Troy, New York. Rensselaer Polytechnic Institute Math Report No. 98–100 (1998)
Bradley, P.S., Mangasarian, O.L.: Feature Selection Via Concave Minimization and Support Vector Machines, Mathematical Programming Technical Report, 98-03 (1998)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Scholkopf, B., Burge, C.J.C., Smola, A.J.: Advanced in Kernel Methods - Support vector Learning. The MIT Press, New York (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Choi, G., Bae, S.J. (2006). Automated Parameter Selection for Support Vector Machine Decision Tree. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_83
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DOI: https://doi.org/10.1007/11893257_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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