Title: Construction of practical teaching data classification model based on ROF-SSA-LGBM and its application in physical education teaching and management

Authors: Xinjiao Zhang

Addresses: Department of Physical Education and Research, Shangluo University, Shangluo, 726000, China

Abstract: Under the background of informatisation, to better assist the management of practical teaching, a data classification model based on ROF-ISSA-LGBM is proposed. Firstly, rotation forest algorithm (ROF) is used to screen importance features of practical teaching dataset. Then, improved sparrow search algorithm (ISSA) is adopted to optimise hyperparameters of lightweight gradient boosting machine (LGBM). Finally, ISSA-LGBM is used as the classifier and applied to the classification of practical teaching datasets. The results reveal that on Haberman and Iris datasets with smaller sample sizes, average accuracy, macro average, and micro average of the constructed model reach 80.96%, 95.38% and 95.44%, respectively, its performance is better than that of commonly used classification models, which means the constructed model has high classification accuracy. Therefore, the constructed model can be used for deep mining of potential information in the dataset of physical education practice teaching data, and shows high classification accuracy.

Keywords: rotating forest algorithm; physical education practice teaching; SSA; sparrow search algorithm; LGBM; lightweight gradient boosting machine; combination model; macro average.

DOI: 10.1504/IJCSM.2025.149899

International Journal of Computing Science and Mathematics, 2025 Vol.22 No.2, pp.192 - 208

Received: 05 Sep 2024
Accepted: 05 Jul 2025
Published online: 17 Nov 2025 *

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