An Improved Post-Pruning Algorithm for Decision Tree

The classification accuracy of a decision tree would be lower when the depth and the nodes exceed a certain size.So it's necessary to reduce the scale of decision tree by using apruning algorithm and ensure the accuracy of classification at the same time.To solve this problem,a kind of post-pruning strategy which evenly considers classification accuracy,classification stability,and the scale of decision tree is proposed on the basis of in-depth study of the existing decision tree pruning algorithm.Experimental results show that this improved post-pruning algorithm can effectively reduce the size of the decision tree,ensure the accuracy and stability,and make the final model more compact.