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A poverty index prediction model for students based on PSO-LightGBM

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  • Junjie Zhu

    (Nanjing University of Aeronautics and Astronautics)

  • Butong Li

    (Nanjing University of Aeronautics and Astronautics)

  • Zilong Wang

    (Nanjing University of Aeronautics and Astronautics)

Abstract

Recognizing underprivileged students is a significant challenge in education. Machine learning algorithms have been increasingly used to develop reliable methods for identifying such students, but practical implementations are rarely reported. This paper explores a combination of the PSO algorithm and LightGBM algorithm, providing insights into the complete development process and comparing different machine learning techniques. Experimental results demonstrate that our proposed model exhibits excellent performance in terms of training efficiency, requiring fewer resources than existing models. Furthermore, the model achieves high prediction accuracy and performs well on various evaluation metrics, such as MAE, MSE, RMSE, and $$R^{2}$$ R 2 .

Suggested Citation

  • Junjie Zhu & Butong Li & Zilong Wang, 2025. "A poverty index prediction model for students based on PSO-LightGBM," Annals of Operations Research, Springer, vol. 348(1), pages 717-734, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-023-05652-4
    DOI: 10.1007/s10479-023-05652-4
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    References listed on IDEAS

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    1. Jianyi Lyu & Peijie Zheng & Yue Qi & Guohua Huang, 2023. "LightGBM-LncLoc: A LightGBM-Based Computational Predictor for Recognizing Long Non-Coding RNA Subcellular Localization," Mathematics, MDPI, vol. 11(3), pages 1-13, January.
    2. Ha, Wei & Yan, Fang, 2018. "Does money matter? The effects of block grants on education attainment in rural China: Evidence from intercensal population survey 2015," International Journal of Educational Development, Elsevier, vol. 62(C), pages 174-183.
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