基于不平衡数据的企业员工流动意向的预测和优化
1Metropolitan College, Boston University, Boston, Massachusetts, United States of America.
PloS one
|August 17, 2023
概括
本研究引入了一种新的方法来预测员工周转率,通过改进支持向量机 (SVM) 与合成少数群体过量采样技术 (SMOTE) 和模糊C-平均 (FCM) 聚类来进行更好的不平衡数据分析.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 业务分析 业务分析
背景情况:
- 员工流动对公司构成重大财务和运营风险.
- 传统的营业额理论与不平衡的数据集扎,导致商业决策有缺陷.
- 标准支持矢量机 (SVM) 模型对于不平衡的数据分类是不理想的.
研究的目的:
- 开发一种先进的分类算法,从不平衡的数据集准确预测员工周转率.
- 通过解决数据不平衡问题来提高支持矢量机 (SVM) 的性能.
- 为企业提供一个可靠的工具,用于积极的员工保留策略.
主要方法:
- 实施合成少数群体过量采样技术 (SMOTE) 来平衡员工流动数据.
- 引入了改进的 Fuzzy C-mean (FCM) 集群结合 SMOTE (IFCM-SMOTE-SVM) 进行更准确的合成数据生成.
- 通过整合内核函数来进行增强的数据转换和分类,开发了一个基于内核空间的分类算法 (KS-IFCM-SMOTE-SVM).
主要成果:
- 拟议的KS-IFCM-SMOTE-SVM算法在分类不平衡的员工数据方面表现稳定且准确.
- 内核空间转换提高了用于SVM分类的合成数据的有效性.
- 实验验证证了算法的跨不同企业数据集的概括能力.
结论:
- KS-IFCM-SMOTE-SVM算法有效地解决了在员工流动预测中数据不平衡的挑战.
- 这种方法可以通过早期识别面临离职风险的员工来加强企业的决策.
- 该研究提供了一个强大的机器学习解决方案,用于改善人力资源管理和降低营业额成本.
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