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ISCIS
2009
Springer

Calculating the VC-dimension of decision trees

13 years 8 months ago
Calculating the VC-dimension of decision trees
—We propose an exhaustive search algorithm that calculates the VC-dimension of univariate decision trees with binary features. The VC-dimension of the univariate decision tree with binary features depends on (i) the VC-dimension values of the left and right subtrees, (ii) the number of inputs, and (iii) the number of nodes in the tree. From a training set of example trees whose VC-dimensions are calculated by exhaustive search, we fit a general regressor to estimate the VC-dimension of any binary tree. These VC-dimension estimates are then used to get VC-generalization bounds for complexity control using SRM in decision trees, i.e., pruning. Our simulation results shows that SRM-pruning using the estimated VC-dimensions finds trees that are as accurate as those pruned using cross-validation.
Ozlem Asian, Olcay Taner Yildiz, Ethem Alpaydin
Added 25 Jul 2010
Updated 25 Jul 2010
Type Conference
Year 2009
Where ISCIS
Authors Ozlem Asian, Olcay Taner Yildiz, Ethem Alpaydin
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