An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Data-driven Calibration of Penalties for Least-Squares Regression
The Journal of Machine Learning Research
Tree pruning with subadditive penalties
IEEE Transactions on Signal Processing
Termination and continuity of greedy growing for tree-structured vector quantizers
IEEE Transactions on Information Theory
Recursive partitioning to reduce distortion
IEEE Transactions on Information Theory
Optimal pruning with applications to tree-structured source coding and modeling
IEEE Transactions on Information Theory
Analysis of a complexity-based pruning scheme for classification trees
IEEE Transactions on Information Theory
Model selection for CART regression trees
IEEE Transactions on Information Theory
Minimax-optimal classification with dyadic decision trees
IEEE Transactions on Information Theory
On the Rate of Convergence of Local Averaging Plug-In Classification Rules Under a Margin Condition
IEEE Transactions on Information Theory
| Hi-index | 0.01 |
Non-asymptotic risk bounds for Classification And Regression Trees (CART) classifiers are obtained in the binary supervised classification framework under a margin assumption on the joint distribution of the covariates and the labels. These risk bounds are derived conditionally on the construction of the maximal binary tree and allow to prove that the linear penalty used in the CART pruning algorithm is valid under the margin condition. It is also shown that, conditionally on the construction of the maximal tree, the final selection by test sample does not alter dramatically the estimation accuracy of the Bayes classifier.