Automated Parameter Selection for Support Vector Machine Decision Tree
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.
KeywordsSupport Vector Machine Decision Node Nonlinear Pattern Automate Scheme Census Income
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- 2.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)Google Scholar
- 3.Bradley, P.S., Mangasarian, O.L.: Feature Selection Via Concave Minimization and Support Vector Machines, Mathematical Programming Technical Report, 98-03 (1998)Google Scholar
- 5.Scholkopf, B., Burge, C.J.C., Smola, A.J.: Advanced in Kernel Methods - Support vector Learning. The MIT Press, New York (1999)Google Scholar