JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (11): 7-12.doi: 10.6040/j.issn.1671-9352.0.2016.238

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Study on feature selection method based on information loss

LI Zhao1,2.3, SUN Zhan-2,3, LI Xiao,2,3, LI Cheng2.3   

  1. 1. School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China;2. Shandong Computer Science Center(National Supercomputer Center in Jinan), Jinan 250014, Shandong, China;3. Shandong Engineering Technology Research Center of Egovernment Big Data, Jinan 250014, Shandong, China;4. Shandong Provincial Key Laboratory of Computer Networks, Jinan 250014, Shandong, China
  • Received:2016-05-26 Online:2016-11-20 Published:2016-11-22

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Abstract: The purpose of this paper is to realize fast feature selection through studying the measure metric between the features and the calculation method of correlation between class variable and selected feature subset. A novel information loss metric based on extended entropy was proposed and used to measure the correlation between features. For avoiding calculating complicated combination mutual information, a novel feature selection method based on information loss was proposed. The method assures that the selected feature can increase the most information of the selected feature set. At last, the proposed method was used to analyze 3 kinds of practical classification dataset downloaded from UCI public dataset. Feature selection results are tested with Support Vector Machine and the results were compared with some other feature selection methods. Comparison results show that the proposed method in this paper is more efficient than others.

Key words: information loss, information bottleneck theory, mutual information, feature selection, extended entropy

CLC Number: 

  • TP311
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