The main methods to prune a decision tree are pre-pruning and post-pruning.
决策树修剪主要有预修剪和后修剪两种方法。
参考来源 - 基于粗糙集理论的决策树预修剪学习算法研究Agree degree decision tree adopts threshold pre-pruning.
赞同度决策树采用阈值预剪枝作为剪枝方法。
参考来源 - 数据挖掘在医疗信息分析中的研究与应用After introducing many methods, this paper illuminates two pre-pruning means: PDTBS and PDTBP designed by author, other experiment based on UCI data show two algorithms can prune decision-tree to large extent on the condition of that accuracy diminish hardly.
本文介绍了许多事前、事后修剪算法，并阐述了本人提出的两种事前修剪算法：基于结点支持度的事前修剪算法PDTBS和基于结点纯度的事前修剪算法PDTBP。 在另一个基于UCI数据的实验中实现了提到的几种修剪算法以及PDTBS和PDTBP，结果表明：后两者在对树分类精度影响极小的条件下，大幅度地修剪了决策树。
参考来源 - 决策树的结点属性选择和修剪方法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
This paper is a brief survey of several methods for decision tree simplification, including the pre-pruning, post-pruning and the other methods.
论文是决策树简化方法的一个综述，包括预剪枝、后剪枝和其它方法。
By using the method to improve the ID3 algorithm, experiments show that the algorithm generates smaller decision tree and USES less training time than the algorithm using pre-pruning method.
利用该方法对基本ID 3决策树算法进行了改进。分析和实验表明，与先剪枝方法相比，该方法能进一步减小决策树的规模和训练时间。
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