...runing):若当前节点的划分不能带来决策树泛化能力的提升,则停止划分并将其标记为叶节点。有欠拟合的风险。 后剪枝(post-pruning):从已经生成的决策树中自底向上对非叶节点考察,若将该节点替换成叶节点,能提升泛化能力,则将该子树替换为叶节点。
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向后剪枝 backward pruning
Secondly, the decision tree algorithm is optimized in this paper in two aspects: attribute reduction and pruning. Attribute reduction algorithm ER based on the degree of dependency of attribute and post-pruning algorithm Prune based on rough set theory are proposed.
其次,从属性约简和剪枝两方面对决策树算法进行优化,提出了基于属性依赖度的属性约简算法ER和基于粗糙集理论的决策树后剪枝算法Prune。
参考来源 - 基于决策树的数据挖掘算法研究与应用·2,447,543篇论文数据，部分数据来源于NoteExpress
不建议在栽种后剪枝。
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.
这避免了后剪枝策略所需的高昂代价，减少了扫描磁盘数据的次数和大量的CPU时间，进一步提高了算法的效率。
This avoid high cost by using post-pruning measure which require many times for scanning disk data and amount of CPU time. So we gain a high efficiency.
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