A Strategy of Merging Branches Based on Margin Enlargement of SVM in Decision Tree Induction

This paper investigates the impact of merging branches on decision tree induction. The main concerns are whether the comprehensibility, the size and the generalization accuracy of a decision tree can be improved if an appropriate merging strategy is selected and applied. Based on information gain principle, this paper theoretically analyzes the complexity of a decision tree before and after merging branches, and designs an algorithm of merging branches MID, which is based on the support vector machine margin enlargement. Experimental results show that the MID has the comprehensibility and the generalization accuracy significantly better than the traditional decision tree algorithm without branch merging.