基于MBSO和MDBO-BP-Adaboost方法的中学学生成绩预测模型
Rencheng Fang1, Tao Zhou1, Baohua Yu1
1School of Information Science and Technology, Shihezi University, Xinjiang, China.
Frontiers in big data
|January 29, 2025
概括
本研究引入了一种新的特征选择方法 (MBSO) 和一个学生绩效预测模型 (MDBO-BP-Adaboost),以改进教育数据分析. 这些方法显著降低了数据的复杂性,并提高了预测准确性,以获得更好的学生成果.
科学领域:
- 教育数据挖掘教育数据挖掘
- 机器学习在教育中的应用
- 人工智能应用程序 人工智能应用程序
背景情况:
- 学生绩效预测对于教育规划和学生发展至关重要.
- 教育数据的高维度对准确的特征选择提出了挑战.
- 减少特征维度对于高效和有效的学生成绩预测至关重要.
研究的目的:
- 提出一个新的封装功能选择模型,改进的二进制蛇优化器 (MBSO).
- 开发一个先进的学生绩效预测模型,MDBO-BP-Adaboost.
- 评估拟议模型在减少特征复杂性和提高预测准确性的有效性.
主要方法:
- 在Mat和Por学生成绩数据上使用改进的二进制蛇优化器 (MBSO) 选择特征.
- 改进的泥甲虫优化算法 (MDBO) 的开发,并增强了初始化和策略.
- 整合MDBO以优化BP神经网络,以便在Adaboost框架中作为弱学习者使用 (MDBO-BP-Adaboost).
主要成果:
- MBSO模型显著减少了学生成绩预测所选特征的数量 (平均为7.90和7.10特征).
- MDBO-BP-Adaboost模型实现了高预测准确性,在学生成绩数据集上R2值为0.930和0.903.
- 对比分析表明MDBO-BP-Adaboost的性能优于XGBoost,BP,BP-Adaboost和DBO-BP-Adaboost的模型.
结论:
- MBSO模型有效地减少了学生成绩特征的维度,简化了预测.
- MDBO-BP-Adaboost模型为预测学生表现提供了一个强大而准确的方法.
- 提出的方法在教育数据分析和预测准确性方面取得了重大进展.
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