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集成学习中特征选择技术 被引量：7

Feature Selection for Ensemble Learning
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摘要 集成学习和特征选择是当前机器学习领域中的研究热点.集成学习通过重复采样可产生个体学习器之间差异度,从而提高个体学习器的泛化能力,特征选择应用到集成学习可进一步提高集成学习技术的效果,该研究有3个方面:数据子集的特征选择、个体学习器的选择和多任务学习.该文对近几年集成学习中特征选择技术的研究进行回顾,尤其对以上3个方面的研究分别进行总结,提出一些共性的技术指导以后的研究. Ensemble learning and feature selection are hot improvement of generalization performance of individuals comes sampling the training set. Feature selection for ensemble learning topics in machine learning studies. The primarily from the diversity caused by recan also improve diversity in three aspects： feature selection for individuals, selective ensemble learning, and multi-task learning. This paper gives an overview of feature selection methods for ensemble learning in recent years, and summarize some general techniques useful in the further studies.
作者 李国正 李丹
机构地区 上海大学计算机工程与科学学院
出处 《上海大学学报（自然科学版）》 CAS CSCD 北大核心 2007年第5期598-604,共7页 Journal of Shanghai University:Natural Science Edition
基金 国家自然科学基金资助项目(20503015) 上海市教委自然科学基金资助项目(05AZ67)
关键词 集成学习 特征选择 多任务学习 ensemble learning feature selection multi-task learning
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献29

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二级参考文献7

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共引文献14

同被引文献64

引证文献7

二级引证文献17

上海大学学报（自然科学版）
2007年 第5期

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