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Gift: granularity over specific-class for feature selection

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Abstract

As a fundamental material of Granular Computing, information granulation sheds new light on the topic of feature selection. Although information granulation has been effectively applied to feature selection, existing feature selection methods lack the characterization of feature potential. Such an ability is one of the important factors in evaluating the importance of features, which determines whether candidate features have sufficient ability to distinguish different target variables. In view of this, a novel concept of granularity over specific-class from the perspective of information granulation is proposed. Essentially, such a granularity is a fusion of intra-class and extra-class based granularities, which enables to exploit the discrimination ability of features. Accordingly, an intuitive yet effective framework named Gift, i.e., granularity over specific-class for feature selection, is proposed. Comprehensive experiments on 29 public datasets clearly validate the effectiveness of Gift as compared with other feature selection strategies, especially in noisy data.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (Nos. 62076111, 62006128, 62006099, 61906078).

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Author notes

  1. Liu Keyu, Yang Xibei and Qian Yuhua have contributed equally to this work.

Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Changhui Road, Zhenjiang, 212100, Jiangsu, China

    Jing Ba, Keyu Liu & Xibei Yang

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Xi’an Road, Chengdu, 611756, Sichuan, China

    Keyu Liu

  3. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Lincheng Street, Zhoushan, 316022, Zhejiang, China

    Xibei Yang

  4. School of Computer and Information Technology, Shanxi University, Wucheng Road, Taiyuan, 030006, Shanxi, China

    Yuhua Qian

Authors
  1. Jing Ba
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  2. Keyu Liu
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  3. Xibei Yang
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  4. Yuhua Qian
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Contributions

JB: Conceptualization, Methodology, Software, Investigation, Writing—Original draft. KL: Formal analysis, Data curation Funding acquisition. XY: Supervision, Resources, Project administration, Validation, Funding acquisition, Writing-Review & Editing. YQ: Formal analysis, Data curation, Software.

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Correspondence to Xibei Yang.

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The authors declare no competing interests.

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Appendix A

Appendix A

See Table 8.

Table 8 A list of variables
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Ba, J., Liu, K., Yang, X. et al. Gift: granularity over specific-class for feature selection. Artif Intell Rev 56, 12201–12232 (2023). https://doi.org/10.1007/s10462-023-10499-z

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