A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
SNIAFL: Towards a static noninteractive approach to feature location
ACM Transactions on Software Engineering and Methodology (TOSEM)
Diversification for better classification trees
Computers and Operations Research
FBP: A Frontier-Based Tree-Pruning Algorithm
INFORMS Journal on Computing
Post-pruning in decision tree induction using multiple performance measures
Computers and Operations Research
Post-pruning in regression tree induction: An integrated approach
Expert Systems with Applications: An International Journal
Constructing a decision tree from data with hierarchical class labels
Expert Systems with Applications: An International Journal
Context-aware system for proactive personalized service based on context history
Expert Systems with Applications: An International Journal
An Optimal Constrained Pruning Strategy for Decision Trees
INFORMS Journal on Computing
A scalable decision tree system and its application in pattern recognition and intrusion detection
Decision Support Systems
Expert Systems with Applications: An International Journal
Comparison of regression tree data mining methods for prediction of mortality in head injury
Expert Systems with Applications: An International Journal
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This paper concerns a decision-tree pruning method, a key issue in the development of decision trees. We propose a new method that applies the classical optimization technique, dynamic programming, to a decision-tree pruning procedure. We show that the proposed method generates a sequence of pruned trees that are optimal with respect to tree size. The dynamic-programming-based pruning (DPP) algorithm is then compared with cost-complexity pruning (CCP) in an experimental study. The results of our study indicate that DPP performs better than CCP in terms of classification accuracy.