Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (14): 158-160.DOI: 10.3778/j.issn.1002-8331.2009.14.048

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Using information gain method to select classifiers

KANG Jun-jian1,2,DU Zai-lin3,ZHANG Xin-dong1,ZHU Qun-ying1   

  1. 1.School of Information and Engineering,Shijiazhuang University of Economics,Shijiazhuang 050031,China
    2.Hebei Key Lab of Optoelectronic Information and Geo-detection Technology,Shijiazhuang 050031,China
    3.Innovative Small and Mediam-sized Fund Management Center,Shijiazhuang 050031,China
  • Received:2009-02-11 Revised:2009-03-31 Online:2009-05-11 Published:2009-05-11
  • Contact: KANG Jun-jian



  1. 1.石家庄经济学院 信息工程学院,石家庄 050031
    2.河北省光电信息与地球探测技术重点实验室,石家庄 050031
    3.河北省科技厅中小企业创新资金管理中心,石家庄 050031
  • 通讯作者: 亢俊健

Abstract: In order to eliminate adverse effect brought by bad classifiers and improve effect and stability of combiners,an approach extracting classifiers based on Information Gain(IG) is proposed.It can reduce classifier space with high dimension,and then learn a combiner in lower dimension.The compared results obtain on multiple public avaiable datasets show that the method is feasible firstly.Secondly,its performance is perfect.

摘要: 为了减少表现差的个体分类器对集成器分类性能的影响,提高集成器分类效果及稳定性,提出了基于信息增益的分类器选择方法。该方法将高维分类器空间压缩至低维分类器空间,并在该空间内学习集成器。在多个数据集上的比较实验结果表明,该方法可行,其集成性能较理想。