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Applied Intelligence

, Volume 49, Issue 3, pp 883–896 | Cite as

Feature selection based on conditional mutual information: minimum conditional relevance and minimum conditional redundancy

  • HongFang ZhouEmail author
  • Yao Zhang
  • YingJie Zhang
  • HongJiang Liu
Article
  • 48 Downloads

Abstract

Feature selection is a process that selects some important features from original feature set. Many existing feature selection algorithms based on information theory concentrate on maximizing relevance and minimizing redundancy. In this paper, relevance and redundancy are extended to conditional relevance and conditional redundancy. Because of the natures of the two conditional relations, they tend to produce more accurate feature relations. A new frame integrating the two conditional relations is built in this paper and two new feature selection methods are proposed, which are Minimum Conditional Relevance-Minimum Conditional Redundancy (MCRMCR) and Minimum Conditional Relevance-Minimum Intra-Class Redundancy (MCRMICR) respectively. The proposed methods can select high class-relevance and low-redundancy features. Experimental results for twelve datasets verify the proposed methods perform better on feature selection and have high classification accuracy.

Keywords

Feature selection Mutual information Conditional redundancy Intra-class redundancy 

Notes

Acknowledgments

The corresponding author would like to thank the support from the National Natural Science Foundation of China under the Grant of 61402363, the Education Department of Shaanxi Province Key Laboratory Project under the Grant of 15JS079, Xi’an Science Program Project under the Grant of 2017080CG/RC043(XALG017), the Ministry of Education of Shaanxi Province Research Project under the Grant of 17JK0534, and Beilin district of Xi’an Science and Technology Project under the Grant of GX1625.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • HongFang Zhou
    • 1
    • 2
    Email author
  • Yao Zhang
    • 1
  • YingJie Zhang
    • 1
  • HongJiang Liu
    • 1
  1. 1.School of Computer Science and EngineeringXi’an University of TechnologyXi’anChina
  2. 2.Shaanxi Key Laboratory of Network Computing and Security TechnologyXi’anChina

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