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A support vector machine approach to decision trees
Bennett, K.P. Blue, J.A.
Rensselaer Polytech. Inst., Troy, NY;

This paper appears in: Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Publication Date: 4-9 May 1998
Volume: 3, On page(s): 2396-2401 vol.3
Meeting Date: 05/04/1998 - 05/09/1998
Location: Anchorage, AK, USA
ISBN: 0-7803-4859-1
References Cited: 10
INSPEC Accession Number: 6047153
Digital Object Identifier: 10.1109/IJCNN.1998.687237
Current Version Published: 2002-08-06

Abstract
Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The “optimal” decision tree is characterized, and both a primal and dual space formulation for constructing the tree are proposed. The result is a method for generating logically simple decision trees with multivariate linear, nonlinear or linear decisions. By varying the kernel function used, the decisions may consist of linear threshold units, polynomials, sigmoidal neural networks, or radial basis function networks. The preliminary results indicate that the method produces simple trees that generalize well with respect to other decision tree algorithms and single support vector machines

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