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Decision tree pruning using an additive information quality measure
Wehenkel, Louis
1993In Bouchon-Meunier, B; Valverde, L; Yager, R (Eds.) Uncertainty in Intelligent Systems
 

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Keywords :
Decision trees; Machine Learning; Pruning
Abstract :
[en] An additive quality measure based on information theory is introduced for the inductive inference of decision trees. It takes into account both the information content and the complexity of a tree, combined so as to evaluate the tree on the basis of its learning sample. The additivity of the quality measure with respect to the decomposition of a tree into subtrees, allows to formulate an efficient recursive backward pruning algorithm to maximize the quality. Simulation results are provided on the ground of a real life problem related to electric power system operation and a synthetic digit recognition problem.
Disciplines :
Computer science
Author, co-author :
Wehenkel, Louis  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Decision tree pruning using an additive information quality measure
Publication date :
1993
Main work title :
Uncertainty in Intelligent Systems
Editor :
Bouchon-Meunier, B
Valverde, L
Yager, R
Publisher :
Elsevier-North Holland
Pages :
397-411
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since 26 May 2012

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