International Society of Science and Applied Technologies |
|
Research on Accurate Identification of Poor Students - A Case Study of Jiangsu Province | ||||
Author | Junjie Zhu
|
|||
Co-Author(s) | Butong Li; Weihao Liu; Xufeng Zhao
|
|||
Abstract | Identifying poor students is a significant problem for all walks of life, especially in education. With the development of machine learning algorithms, researchers have been finding progressively sophisticated ways to recognize poor students, but practical implementations are rarely reported. The paper explores the combination of the PSO algorithm and the LightGBM algorithm, gives insights into the complete development process, and compares different machine learning methods. The experimental results reveal that our proposed poverty prediction index model based on PSO-LightGBM has an excellent performance in training resource consumption, model evaluation, and prediction accuracy.
|
|||
Keywords | Machine learning, PSO, LightGBM, Identification of poor students | |||
Article #: DSBFI23-116 |
January 8-10, 2023 - Da Nang, Vietnam |