International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
C4.5: programs for machine learning
C4.5: programs for machine learning
A Comparative Analysis of Methods for Pruning Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalization Bounds for Decision Trees
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Tree pruning with subadditive penalties
IEEE Transactions on Signal Processing
Analysis of a complexity-based pruning scheme for classification trees
IEEE Transactions on Information Theory
Minimax-optimal classification with dyadic decision trees
IEEE Transactions on Information Theory
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Recent work on decision tree pruning[1] has brought to the attention of the machine learning community the fact that, in classification problems, the use of subadditive penalties in cost-complexity pruning has a stronger theoretical basis than the usual additive penalty terms. We implement cost-complexity pruning algorithms with general size-dependent penalties to confirm the results of[1] . Namely, that the family of pruned subtrees selected by pruning with a subadditive penalty of increasing strength is a subset of the family selected using additive penalties. Consequently, this family of pruned trees is unique, it is nested and it can be computed efficiently. However, in spite of the better theoretical grounding of cost-complexity pruning with subadditive penalties, we found no systematic improvements in the generalization performance of the final classification tree selected by cross-validation using subadditive penalties instead of the commonly used additive ones.