Applied Intelligence

, Volume 48, Issue 12, pp 4615–4625 | Cite as

Feature selection considering weighted relevancy

  • Ping Zhang
  • Wanfu Gao
  • Guixia LiuEmail author


Feature selection plays an important role in pattern recognition and machine learning. Feature selection based on information theory intends to preserve the feature relevancy between features and class labels while eliminating irrelevant and redundant features. Previous feature selection methods have offered various explanations for feature relevancy, but they ignored the relationships between candidate feature relevancy and selected feature relevancy. To fill this gap, we propose a feature selection method named Feature Selection based on Weighted Relevancy (WRFS). In WRFS, we introduce two weight coefficients that use mutual information and joint mutual information to balance the importance between the two kinds of feature relevancy terms. To evaluate the classification performance of our method, WRFS is compared to three competing feature selection methods and three state-of-the-art methods by two different classifiers on 18 benchmark data sets. The experimental results indicate that WRFS outperforms the other baselines in terms of the classification accuracy, AUC and F1 score.


Feature selection Classification Information theory Weighted relevancy Mutual information 



This work was supported by the National Nature Science Foundation of China [grant number61772226,61373051, 61502343]; Science and Technology Development Program of Jilin Province [grant number 20140204004GX]; Science Research Funds for the Guangxi Universities [grant number KY2015ZD122]; Science Research Funds for the Wuzhou University [grant number 2014A002]; Project of Science and Technology Innovation Platform of Computing and Software Science (985 Engineering); Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China; Fundamental Research Funds for the Central.


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

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJiLin UniversityChangchunPeople’s Republic of China
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunPeople’s Republic of China

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