Journal Article10.1287/IJOC.13.4.332.9732
A Dynamic Programming Based Pruning Method for Decision Trees
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TL;DR: A new method that applies the classical optimization technique, dynamic programming, to a decision-tree pruning procedure is proposed and it is shown that the proposed method generates a sequence of pruned trees that are optimal with respect to tree size.
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Abstract: 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.
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