Data mining criteria for tree-based regression and classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
A Dynamic Programming Based Pruning Method for Decision Trees
INFORMS Journal on Computing
A finite-sample simulation study of cross validation in tree-based models
Information Technology and Management
Decision tree models for characterizing smoking patterns of older adults
Expert Systems with Applications: An International Journal
Data mining modeling on the environmental impact of airport deicing activities
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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A frontier-based tree-pruning algorithm (FBP) is proposed. The new method has an order of computational complexity comparable to cost-complexity pruning (CCP). Regarding tree pruning, it provides a full spectrum of information: namely, (1) given the value of the penalization parameter λ, it gives the decision tree specified by the complexity-penalization approach; (2) given the size of a decision tree, it provides the range of the penalization parameter λ, within which the complexity-penalization approach renders this tree size; (3) it finds the tree sizes that are inadmissible---no matter what the value of the penalty parameter is, the resulting tree based on a complexity-penalization framework will never have these sizes. Simulations on real data sets reveal a “surprise:” in the complexity-penalization approach, most of the tree sizes are inadmissible. FBP facilitates a more faithful implementation of cross validation (CV), which is favored by simulations. Using FBP, a stability analysis of CV is proposed.