期刊文献+

优化的互信息特征选择方法 被引量:12

Optimized mutual information feature selection method
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摘要 在文本分类中,互信息是一种被广泛应用的特征选择方法,但是该方法仅考虑了特征的文档频而没有考虑特征的词频,导致它经常倾向于选择出现频率较低的特征。为此,提出了一个新的文档频并把它引入到互信息方法中,从而获得了一种优化的互信息方法。该优化的互信息方法不但考虑了特征的文档频而且还考虑了特征出现的词频。实验结果表明该优化的互信息方法性能良好。 Mutual Information(MI) is a feature selection method that is used widely in text categorization,but this method only takes into account document frequency of selected features and ignores word frequency of selected features.For this reason,MI is inclined to select lower-frequency features.In this case,a new document frequency is presented and introduced into MI, so that an optimized MI method is proposed.The optimized MI not only pays attention to document frequency of selected features but also attaches importance to word frequency of selected features.The experimental results show that the optimized MI is promising.
出处 《计算机工程与应用》 CSCD 北大核心 2010年第26期122-124,共3页 Computer Engineering and Applications
基金 四川省科技计划项目(No.2008GZ0003) 四川省科技攻关计划No.07GG006-019~~
关键词 文本分类 互信息 特征选择 词频 文档频 text categorization Mutual Information (MI) feature selection word frequency document frequency
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参考文献8

  • 1Nguyen M H, Torre F D.Optimal feature selection for support vector machines[J].Pattem Recognition,2010,43(3) : 584-591.
  • 2Liu Hua-wen, Sun Ji-gui, Liu Lei.Feature selection with dynamic mutual information[J].Pattem Recognition,2009,42(7) : 1330-1339.
  • 3朱颢东,钟勇.基于粗糙集和灰色关联度的综合性特征选择[J].计算机工程与应用,2009,45(35):6-9. 被引量:5
  • 4Zhu Hao-dong,Zhao Xiang-hui,Zhong Yong.Feature selection method combined optimized document frequency with improved RBF netWork[C]//Proc of.5th International Cnference, ADMA 2009, Beijing, China, August 2009:796-803.
  • 5XU Yan.A formal study of feature selection in text categorization[J].通讯和计算机(中英文版),2009,6(4):32-41. 被引量:15
  • 6Kalousis A,Prados J, Hilario M.Stability of feature selection algorithms: A study on high-dimensional spaces[J].Knowledge and Information Systems, 2007,12 ( 1 ) : 95-116.
  • 7Destrero A,Mosci S, Mol C D.Feature selection for high-dimensional data[J].Computational Management Science, 2009, 6 (1): 25-40.
  • 8Bakus J,Kamel M S.Higher order feature selection for text classification[J].Knowledge and Information Systems, 2006, 9(4): 468 -491.

二级参考文献8

  • 1胡佳妮,徐蔚然,郭军,邓伟洪.中文文本分类中的特征选择算法研究[J].光通信研究,2005(3):44-46. 被引量:48
  • 2张海龙,王莲芝.自动文本分类特征选择方法研究[J].计算机工程与设计,2006,27(20):3840-3841. 被引量:45
  • 3Delgado M,Martin-Bautista M J,Sanchez D,et al.Mining text data: Special features and patterns[C]//Proceedings of ESF Exploratory Workshop.London, U.K, Sept.2002 : 32-38.
  • 4Friedman N,Geiger D,Goldszmidt M.Bayesian network classifiers[J]. Machine Learning, 1997,29(2): 131-163.
  • 5Pawlak Z.Rough sets[J].International Journal of Information and Computer Sciences, 1982,11 (5) : 341-383.
  • 6Liang Jiye,CHIN K S,Dang Chuangyin,et al.A new method for measuring uncertainty and fuzziness in rough set theory[J].International Journal of General Systems,2002,31(4):331-342.
  • 7曾黄麟.智能计算[M].重庆:重庆大学出版社,2004..
  • 8周茜,赵明生,扈旻.中文文本分类中的特征选择研究[J].中文信息学报,2004,18(3):17-23. 被引量:166

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