加强学生职业指导和情感分析:以绩效为导向的混合学习方法,具有特征排名
Sana Yaqoob1, Ayman Noor2, Talal H Noor2
1Department of Computer Science and Information Technology, University of Lahore, Lahore, Pakistan.
PloS one
|May 9, 2025
概括
本研究介绍了一种混合学习技术,用于准确的职业预测,改善特征选择和排名. 该方法提高了预测的准确性,帮助学生选择正确的职业道路.
科学领域:
- 机器学习 机器学习
- 职业发展 职业发展
- 数据科学数据科学数据科学
背景情况:
- 选择职业道路对学生来说是一个重大挑战,特别是在时间限制下.
- 现有的职业预测模型往往缺乏准确性,原因是缺少最佳特征选择和优先级.
研究的目的:
- 通过整合额外的属性和完善功能选择,开发一种增强的职业预测方法.
- 准确地确定,排序和优先考虑相关功能,以提高预测准确度.
- 消除非必要的特征,简化预测过程.
主要方法:
- 功能融合和规范化 (FFN) 用于精确的数据识别.
- 平均特征排名 (AFR) 使用随机森林 (RF) 和线性回归 (LR) 进行特征优先排名.
- 用加权特征 (PWF) 改进预测,整合主要组件 (PC) 分析以减少特征.
- 一个混合多层感知器 (MLP) 分类器与5倍交叉验证被用于性能评估.
主要成果:
- 混合方法确定了职业预测的优越特征集.
- 排名前十二的特征是根据平均射频得分和LR系数排名的.
- 预测准确度 (ACC) 为87%,精度 (P) 为87%,回忆率 (R) 为86%,F1得分为86%.
- 接收器运行特征曲线下的面积 (AUC-ROC) 达到92%.
结论:
- 拟议的混合学习技术在准确预测职业发展轨迹方面表现出高效.
- 精细的特征选择和优先级显著提高预测性能.
- 这种方法为面对职业道路选择挑战的学生提供了强大的解决方案.
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