New selective ensemble learning method for decision tree

doi:10.3778/j.issn.1002-8331.2010.17.012

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (17): 41-44.DOI: 10.3778/j.issn.1002-8331.2010.17.012

• 研究、探讨 • Previous Articles Next Articles

New selective ensemble learning method for decision tree

ZHANG Yan-ping¹，CAO Zhen-tian¹，ZHAO Shu¹，ZHENG Yao-jun²，DU Ling¹，DOU Rong-rong¹

1.MOE Key Lab of Intelligent Computing & Signal Processing，Anhui University，Hefei 230039，China
2.Datang Generating Corporation in Guizhou，Guiyang 550002，China

Received:2008-12-18 Revised:2009-02-24 Online:2010-06-11 Published:2010-06-11
Contact: ZHANG Yan-ping

一种新的决策树选择性集成学习方法

张燕平¹，曹振田¹，赵姝¹，郑尧军²，杜玲¹，窦蓉蓉¹

1.安徽大学智能计算与信号处理教育部重点实验室，合肥 230039
2.大唐贵州发电有限公司，贵阳 550002

通讯作者: 张燕平

Abstract

Abstract: Diversity among the individual learners is deemed to be a key issue in ensemble learning.Popular ensemble learning algorithms such that Bagging adopts re-sampling technology to produce the diversity of the individual learners.Selective ensemble chooses individual learners which are a part of the original learners to ensemble.The result shows that it is better than the original ensemble.However，how to choose learners is a problem.Using Q statistic to measure the diversity of a pair of learners，a new selective ensemble learning method for decision tree is proposed.Compared with the C4.5 and Bagging method，it works better.

Key words: ensemble learning, selective ensemble, Q statistic

摘要： 个体学习器的差异度是集成学习中的关键因素。流行的集成学习算法如Bagging通过重取样技术产生个体学习器的差异度。选择性集成从集成学习算法产生的个体学习器中选择一部分来集成，结果表明比原集成更好。但如何选择学习器是个难题。使用Q统计量度量两个学习器的差异度，提出一种新的决策树选择性集成学习方法。与C4.5，Bagging方法相比，表现出很好的效果。

关键词: 集成学习, 选择性集成, Q统计量

CLC Number:

TP391.4

ZHANG Yan-ping¹，CAO Zhen-tian¹，ZHAO Shu¹，ZHENG Yao-jun²，DU Ling¹，DOU Rong-rong¹. New selective ensemble learning method for decision tree[J]. Computer Engineering and Applications, 2010, 46(17): 41-44.

张燕平¹，曹振田¹，赵姝¹，郑尧军²，杜玲¹，窦蓉蓉¹. 一种新的决策树选择性集成学习方法[J]. 计算机工程与应用, 2010, 46(17): 41-44.

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New selective ensemble learning method for decision tree

一种新的决策树选择性集成学习方法

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