Pruning Decision Trees using Rules3 Inductive Learning Algorithm
King Saud University, College of Computer and Information Sciences, P.O.Box 51178, Riyadh, 11543, Saudi Arabia
Math. Comput. Appl. 2005, 10(1), 113-120; https://doi.org/10.3390/mca10010113
Published: 1 April 2005
Abstract
One important disadvantage of decision tree based inductive learning algorithms is that they use some irrelevant values to establish the decision tree. This causes the final rule set to be less general. To overcome with this problem the tree has to be pruned. In this article using the recently developed RULES inductive learning algorithm, pruning of a decision tree is explained. The decision tree is extracted for an example problem using the ID3 algorithm and then is pruned using RULES. The results obtained before and after pruning are compared. This shows that the pruned decision tree is more general.
Keywords:
Pruning; Decision Trees; Inductive Learning
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MDPI and ACS Style
Aksoy, M.S. Pruning Decision Trees using Rules3 Inductive Learning Algorithm. Math. Comput. Appl. 2005, 10, 113-120. https://doi.org/10.3390/mca10010113
AMA Style
Aksoy MS. Pruning Decision Trees using Rules3 Inductive Learning Algorithm. Mathematical and Computational Applications. 2005; 10(1):113-120. https://doi.org/10.3390/mca10010113
Chicago/Turabian StyleAksoy, Mehmet Sabih. 2005. "Pruning Decision Trees using Rules3 Inductive Learning Algorithm" Mathematical and Computational Applications 10, no. 1: 113-120. https://doi.org/10.3390/mca10010113
