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A Clustering Based Feature Selection Method Using Feature Information Distance for Text Data

  • Shilong Chao
  • Jie Cai
  • Sheng YangEmail author
  • Shulin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

Feature selection is a key point in text classification. In this paper a new feature selection method based on feature clustering using information distance is put forward. This method using information distance measure builds a feature clusters space. Firstly, K-medoids clustering algorithm is employed to gather the features into k clusters. Secondly the feature which has the largest mutual information with class is selected from each cluster to make up a feature subset. Finally, choose target number features according to the mRMR algorithm from the selected subset. This algorithm fully considers the diversity between features. Unlike the incremental search algorithm mRMR, it avoids prematurely falling into local optimum. Experimental results show that the features selected by the proposed algorithm can gain better classification accuracy.

Keywords

Text classification Feature selection Cluster Diversity 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant No. 61472467).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shilong Chao
    • 1
  • Jie Cai
    • 1
  • Sheng Yang
    • 1
    Email author
  • Shulin Wang
    • 1
  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityChangshaChina

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