Pruning Decision Trees Using Rules3 Inductive Learning Algorithm

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