上海大学学报(自然科学版)

• 计算机工程与科学 • 上一篇    下一篇

集成学习中特征选择技术

李国正,李丹   

  1. 上海大学 计算机工程与科学学院,上海 200072
  • 收稿日期:2007-04-05 修回日期:1900-01-01 出版日期:2007-10-20 发布日期:2007-10-20
  • 通讯作者: 李国正

Feature Selection for Ensemble Learning

LI Guo-zheng,LI Dan   

  1. School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China
  • Received:2007-04-05 Revised:1900-01-01 Online:2007-10-20 Published:2007-10-20
  • Contact: LI Guo-zheng

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.

关键词: 多任务学习, 集成学习, 特征选择

Abstract: Ensemble learning and feature selection are hot topics in machine learning studies. The improvement of generalization performance of individuals comes primarily from the diversity caused by re-sampling the training set. Feature selection for ensemble learning can 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.

Key words: feature selection, multi-task learning, ensemble learning