树的简化是决策树归纳学习中关键的部分。
Simplifying trees is the key part of decision tree induction learning.
论文提出了一种健壮有效的决策树改进模型R - C4.5及其简化版本。
In this paper, a robust and effective decision tree improved model R-C4.5 and its simplified version are introduced.
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。
This paper is a brief survey of several methods for decision tree simplification, including the pre-pruning, post-pruning and the other methods.
决策树简化是决策树学习算法中的一个重要分支。
Decision tree simplification is a significant branch in the study of decision-tree learning algorithms.
通过实例将前向决策树算法与经典的ID 3算法进行了比较，结果表明针对某些特定的问题前者在保证分类精度不降低的同时也简化了决策树。
Compared with the classical ID3 algorithm through an example, the former can reduce the decision tree at the same time of making sure of improving classification accuracy in some certain problem.
通过实例将前向决策树算法与经典的ID 3算法进行了比较，结果表明针对某些特定的问题前者在保证分类精度不降低的同时也简化了决策树。
Compared with the classical ID3 algorithm through an example, the former can reduce the decision tree at the same time of making sure of improving classification accuracy in some certain problem.
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