在机器学习技术中采用人工蜂和殖民地优化作为认知神经科学工具
Kajal Mahawar1, Punam Rattan1, Ammar Jalamneh2
1Lovely Professional University, Phagwara, 144411, Punjab, India.
Scientific reports
|March 25, 2025
概括
这项研究增强了使用机器学习的IT学生的学术预测. 决策树模型,通过合成少数人过量采样技术 (SMOTE) 和殖民地优化 (ACO) 优化,在预测学生成功方面取得了最佳表现.
科学领域:
- 计算机科学 计算机科学
- 教育技术的教育技术
- 数据科学数据科学数据科学
背景情况:
- 信息技术 (IT) 学生的学术成绩预测对于确定影响因素和改进领域至关重要.
- 现有的预测系统面临着数据不平衡和算法调整的挑战,阻碍了准确性.
- 需要有效的学生学术预测系统来支持教育工作者预测学生的成绩.
研究的目的:
- 为了提高IT学生学业绩预测的准确性.
- 解决不平衡数据的挑战,并优化机器学习算法进行预测.
- 确定影响IT学生学业成功的关键特征.
主要方法:
- 应用机器学习算法,包括决策树 (DT),k-最近邻居和XGBoost.
- 使用合成少数群体过量采样技术 (SMOTE) 来处理不平衡的数据集.
- 雇佣群优化 (ACO) 和人工蜂群优化用于超参数调整.
- 使用肯德尔相关系数分析特征相关性.
主要成果:
- 与决策树 (DT) 模型相结合的SMOTE和ACO在学术预测方面表现优异,与其他测试模型相比.
- 超参数调整显著提高了机器学习模型的预测能力.
- 通过相关性分析确定了通过相关性分析对IT学生学术成功产生积极和负面影响的关键特征.
结论:
- 拟议的方法有效地解决了IT学生学术预测中的不平衡数据和算法调整问题.
- 优化的决策树模型为预测学生学业成绩提供了强大而准确的方法.
- 调查结果为教育机构提供了有价值的见解,以制定针对学生的有针对性的支持策略.
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