논문 상세보기
As a non-parametric data mining method, decision tree classification has performed well in many applications. The complexity of the model increases as the decision tree algorithm proceeds to grow the decision tree as the rule of decision making. While the increase of the complexity enhances the accuracy, it degrades the generalization which predicts the unseen data. This phenomenon is called as overfitting. To avoid the overfitting, pruning has been introduced. Pruning enables to make the generalization better, reduces the complexity, and avoids the overfitting. Although various pruning methods have been proposed, selecting the best pruning methods or making balance between complexity and generalization with pruning is not a simple problem. In this paper, we explore the methods of pruning and analyze them to suggest the optimal approach for applications.
