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  • 时雷,段其国,张娟娟,熊明阳,席磊,马新明.基于粗糙集的决策树集成学习算法[J].广西科学,2018,25(4):423-427.    [点击复制]
  • SHI Lei,DUAN Qiguo,ZHANG Juanjuan,XIONG Mingyang,XI Lei,MA Xinming.Decision Tree Ensemble Learning Algorithm Based on Rough Set[J].Guangxi Sciences,2018,25(4):423-427.   [点击复制]
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基于粗糙集的决策树集成学习算法
时雷, 段其国, 张娟娟, 熊明阳, 席磊, 马新明
0
((河南农业大学信息与管理科学学院, 河南粮食作物协同创新中心, 河南郑州 450002))
摘要:
[目的]为提高决策树集成的泛化能力和效率,解决集成全部决策树的情况下有时并不显著提高精度、反而导致额外存储和计算开销的问题,提出一种基于粗糙集的决策树集成学习算法。[方法]该算法基于粗糙集理论,从训练的全部决策树中选择一部分进行集成。[结果]与目前流行的集成学习算法Bagging和Boosting相比,本文提出的算法有效地减小了集成规模,并获得更好的泛化能力。[结论]该算法提高了决策树集成的泛化能力和效率。
关键词:  集成学习  粗糙集  决策树  Bagging  Boosting
DOI:10.13656/j.cnki.gxkx.20180727.001
投稿时间:2018-06-24修订日期:2018-07-26
基金项目:国家自然科学基金(31501225),河南省高等学校重点科研项目(16A520055),河南省现代农业产业技术体系(S2010-01-G04),国家重点研发计划(2016YFD0300609),粮食丰产增效科技创新专项(SQ2017YFNC050081),国家留学基金资助(201709160005)和河南省科技攻关项目(162102110120)资助
Decision Tree Ensemble Learning Algorithm Based on Rough Set
SHI Lei, DUAN Qiguo, ZHANG Juanjuan, XIONG Mingyang, XI Lei, MA Xinming
((College of Information and Management Science, Henan Agricultural University/Collaborative Innovation Center of Henan Grain Crops, Zhengzhou, Henan, 450002, China))
Abstract:
[Objective] The research of the paper focuses on the improvement of the generalization ability and efficiency of ensemble, and resolves the problems that aggregating all decision trees in ensemble usually improves the accuracy of classification slightly, but leads to extra memory costs and computational times. A decision tree ensemble learning algorithm based on rough set is proposed in this paper.[Methods] The algorithm is based on the rough set theory and selects a part from all the decision trees of the training for integration.[Results] The experiment results show that compared with the current popular ensemble learning algorithm Bagging and Boosting, the proposed algorithm not only effectively reduces the scale of ensemble but also obtains stronger generalization ability.[Conclusion] The algorithm improves the generalization ability and efficiency of decision tree integration.
Key words:  ensemble learning  rough set  decision tree  Bagging  Boosting

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