NAWOA-XGBoost: A Novel Model for Early Prediction of Academic Potential in Computer Science Students
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912750686437376 |
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| author | Wei, Junhao Gu, Yanzhao Zhang, Ran Huang, Mingjing Song, Jinhong Li, Yanxiao Zhu, Wenxuan Wang, Yapeng Li, Zikun Wang, Zhiwen Yang, Xu Cheong, Ngai |
| author_facet | Wei, Junhao Gu, Yanzhao Zhang, Ran Huang, Mingjing Song, Jinhong Li, Yanxiao Zhu, Wenxuan Wang, Yapeng Li, Zikun Wang, Zhiwen Yang, Xu Cheong, Ngai |
| contents | Whale Optimization Algorithm (WOA) suffers from limited global search ability, slow convergence, and tendency to fall into local optima, restricting its effectiveness in hyperparameter optimization for machine learning models. To address these issues, this study proposes a Nonlinear Adaptive Whale Optimization Algorithm (NAWOA), which integrates strategies such as Good Nodes Set initialization, Leader-Followers Foraging, Dynamic Encircling Prey, Triangular Hunting, and a nonlinear convergence factor to enhance exploration, exploitation, and convergence stability. Experiments on 23 benchmark functions demonstrate NAWOA's superior optimization capability and robustness. Based on this optimizer, an NAWOA-XGBoost model was developed to predict academic potential using data from 495 Computer Science undergraduates at Macao Polytechnic University (2009-2019). Results show that NAWOA-XGBoost outperforms traditional XGBoost and WOA-XGBoost across key metrics, including Accuracy (0.8148), Macro F1 (0.8101), AUC (0.8932), and G-Mean (0.8172), demonstrating strong adaptability on multi-class imbalanced datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NAWOA-XGBoost: A Novel Model for Early Prediction of Academic Potential in Computer Science Students Wei, Junhao Gu, Yanzhao Zhang, Ran Huang, Mingjing Song, Jinhong Li, Yanxiao Zhu, Wenxuan Wang, Yapeng Li, Zikun Wang, Zhiwen Yang, Xu Cheong, Ngai Computational Engineering, Finance, and Science Whale Optimization Algorithm (WOA) suffers from limited global search ability, slow convergence, and tendency to fall into local optima, restricting its effectiveness in hyperparameter optimization for machine learning models. To address these issues, this study proposes a Nonlinear Adaptive Whale Optimization Algorithm (NAWOA), which integrates strategies such as Good Nodes Set initialization, Leader-Followers Foraging, Dynamic Encircling Prey, Triangular Hunting, and a nonlinear convergence factor to enhance exploration, exploitation, and convergence stability. Experiments on 23 benchmark functions demonstrate NAWOA's superior optimization capability and robustness. Based on this optimizer, an NAWOA-XGBoost model was developed to predict academic potential using data from 495 Computer Science undergraduates at Macao Polytechnic University (2009-2019). Results show that NAWOA-XGBoost outperforms traditional XGBoost and WOA-XGBoost across key metrics, including Accuracy (0.8148), Macro F1 (0.8101), AUC (0.8932), and G-Mean (0.8172), demonstrating strong adaptability on multi-class imbalanced datasets. |
| title | NAWOA-XGBoost: A Novel Model for Early Prediction of Academic Potential in Computer Science Students |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2512.04751 |