• DocumentCode
    3759438
  • Title

    Improved Feature Selection Based on Normalized Mutual Information

  • Author

    Li Yin;Ma Xingfei;Yang Mengxi;Zhao Wei;Gu Wenqiang

  • Author_Institution
    Educ. Inf. Res. Center, WuXi Vocation Inst. of Commerce, Wuxi, China
  • fYear
    2015
  • Firstpage
    518
  • Lastpage
    522
  • Abstract
    For the question (NMIFS) algorithm has the disadvantages of redundancy. This paper introduces a new feature selection method by enhanced NMIFS algorithm. A new quality estimation function is introduced in the new feature selection algorithm to overcome the shortcomings of the classic NMIFS, and the experiment shows on that normalized mutual information feature selection The experiment shows that the INMIFS can generate impressive results in accuracy and redundancy.
  • Keywords
    "Mutual information","Redundancy","Classification algorithms","Entropy","Training","Bayes methods","Decision trees"
  • Publisher
    ieee
  • Conference_Titel
    Distributed Computing and Applications for Business Engineering and Science (DCABES), 2015 14th International Symposium on
  • Type

    conf

  • DOI
    10.1109/DCABES.2015.135
  • Filename
    7429669