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

Junjie Zhu (), Butong Li () and Zilong Wang ()
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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

Annals of Operations Research, 2025, vol. 348, issue 1, No 28, 717-734

Abstract: 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 .

Keywords: Machine learning; PSO; LightGBM; Prediction model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05652-4

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