Experiments with an innovative tree pruning algorithm
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Discriminative wavelet packet filter bank selection for pattern recognition
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Computation of the complexity of vector quantizers by affine modeling
Signal Processing
On signal representations within the Bayes decision framework
Pattern Recognition
Evaluation of decision tree pruning with subadditive penalties
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Risk bounds for CART classifiers under a margin condition
Pattern Recognition
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A complexity-based pruning procedure for classification trees is described, and bounds on its finite sample performance are established. The procedure selects a subtree of a (possibly random) initial tree in order to minimize a complexity penalized measure of empirical risk. The complexity assigned to a subtree is proportional to the square root of its size. Two cases are considered. In the first, the growing and pruning data sets are identical, and in the second, they are independent Using the performance bound, the Bayes risk consistency of pruned trees obtained via the procedure is established when the sequence of initial trees satisfies suitable geometric and structural constraints. The pruning method and its analysis are motivated by work on adaptive model selection using complexity regularization.