Forest Pruning Based on Branch Importance.
Forest Pruning Based on Branch Importance.
复制标题
基于枝条重要性的森林修剪
DOI：
10.1155/2017/3162571
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发表时间：
2017
影响因子：
--
通讯作者：
Guo H
中科院分区：
文献类型：
--
作者：
Jiang X;Wu CA;Guo H
森林是以决策树为成员的集合。提出了一种新的森林剪枝策略，以提高集成泛化能力，减小集成规模。与传统的集成修剪方法不同，该方法试图使用一种称为重要性增益的新建议度量来评估树的分支相对于整个集成的重要性。通过考虑集成精度和集成成员的多样性来设计分支的重要性，从而该度量合理地评估当修剪分支时可以实现集成精度的改善程度。实验结果表明，无论是采用Bagging算法构造的集成还是采用集成选择算法获得的集成，无论是对决策树进行剪枝还是不剪枝，该方法都能显著减小集成规模，提高集成精度.
A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.