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Neighbourhood discernibility degree-based semisupervised feature selection for partially labelled mixed-type data with granular ball

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Abstract

Feature selection can effectively decrease data dimensions by selecting a relevant feature subset. Rough set theory provides a powerful theoretical framework for the feature selection of categorical data with complete labels. However, in reality, the given datasets have only a small number of objects with label information and many unlabelled objects. Furthermore, most of feature selection approaches are computationally expensive. To address the above problems, a semisupervised feature selection algorithm based on neighbourhood discernibility with pseudolabelled granular balls is proposed. First, the set of granular balls based on the purity is generated, which reduces the universe space by sampling. Then, the neighbourhood discernibility is proposed to validate the importance of the candidate features for both labelled and unlabelled objects. Finally, an ensemble voting algorithm is designed to execute feature selection, and a feature subset with satisfactory performance is selected fairly not arbitrarily. On UCI datasets, experimental results verify the advantage of the proposed feature selection algorithm in terms of the feature subset size, classification accuracy and computational time against other algorithms.

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Data availability and access

The data that support the findings of this study are openly available in the UCI machine learning repository at http://archive.ics.uci.edu/ml, reference number [44].

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.62266018 and No.61966016), the Natural Science Foundation of Jiangxi Province, China (No.20224BAB202020), and the National Key Research and Development Program of China (No.2020YFD1100605).

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Authors and Affiliations

  1. School of Information Engineering, East China Jiaotong University, Nanchang, China

    Wenhao Shu, Jianhui Yu & Ting Chen

  2. School of Software, Jiangxi Agricultural University, Nanchang, China

    Wenbin Qian

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  1. Wenhao Shu
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  2. Jianhui Yu
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  3. Ting Chen
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  4. Wenbin Qian
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Contributions

Conceptualization: Wenhao Shu; Jianhui Yu; Methodology: Jianhui Yu; Writing – original draft preparation: Jianhui Yu, Ting Chen; Writing – review and editing: Jianhui Yu,Wenhao Shu; Funding acquisition: Wenbin Qian.

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Correspondence to Wenbin Qian.

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Shu, W., Yu, J., Chen, T. et al. Neighbourhood discernibility degree-based semisupervised feature selection for partially labelled mixed-type data with granular ball. Appl Intell 53, 22467–22487 (2023). https://doi.org/10.1007/s10489-023-04657-7

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