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Leveraging Local Density Decision Labeling and Fuzzy Dependency for Semi-supervised Feature Selection

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Abstract

In real-world scenarios, datasets often lack full supervision due to the high cost associated with acquiring decision labels. Completing datasets by filling in missing labels is essential for preserving the valuable feature information of individual samples. Furthermore, in the era of big data, datasets tend to exhibit high dimensionality, which adds complexity to subsequent data processing. In this study, a new semi-supervised feature selection technique is introduced. Firstly, a fully supervised dataset is created by utilizing a local density decision-labeling algorithm to fill in missing decision labels within the semi-supervised dataset. Next, a fuzzy dependency-based feature selection approach is presented to find and keep the most pertinent characteristics for the finished datasets. Finally, the effectiveness and reliability of our proposed method are validated through a series of rigorous experiments.

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Acknowledgements

We would like to thank the Editor-in-Chief, editors, and anonymous reviewers for their insightful and constructive comments, which have greatly aided us in improving the quality of the paper. This work was supported by the National Natural Science Foundation of China (Grant nos. 12261010, 12326353), the Natural Science Foundation of Guangxi (2023GXNSFBA026019), the Key Laboratory of Software Engineering in Guangxi MinZu University (2022-18XJSY-03), the Postdoctoral Fellowship Program of CPSF (no. GZB20230092), the China Postdoctoral Science Foundation (no. 2023M740383), the Natural Science Foundation of Sichuan Province (no. 24NSFSC1654).

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Authors and Affiliations

  1. School of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, People’s Republic of China

    Gangqiang Zhang

  2. School of Electronic Network, Jiangsu Vocational College of Electronics and Information, Huaian, 223001, People’s Republic of China

    Jingjing Hu

  3. School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People’s Republic of China

    Pengfei Zhang

Authors
  1. Gangqiang Zhang
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  2. Jingjing Hu
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  3. Pengfei Zhang
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Contributions

GZ: Conceptualization, Methodology, Software, Data curation, Writing-original draft. JH: Software, Visualization. PZ: Methodology, Writing-review and editing.

Corresponding author

Correspondence to Pengfei Zhang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Zhang, G., Hu, J. & Zhang, P. Leveraging Local Density Decision Labeling and Fuzzy Dependency for Semi-supervised Feature Selection. Int. J. Fuzzy Syst. 26, 2805–2820 (2024). https://doi.org/10.1007/s40815-024-01740-0

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