预测员工绩效的集成学习框架

P. Sujatha, R.S. Dhivya
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引用次数: 2

摘要

本研究的主要目的是分析员工绩效与组织成长的关系。本研究从收集员工信息开始,然后对收集到的数据进行深入的探索性数据分析,以确定对组织的重要贡献。最后,设计了集成学习框架,以识别员工对组织的潜力。据我们所知,这是第一次将员工绩效的所有方面都纳入数据集。此外,我们还使用了来自金奈知名跨国公司的实时员工数据集。结果表明,所提出的极端梯度增强集成学习算法具有较好的学习效果。
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Ensemble Learning Framework to Predict the Employee Performance
The main goal of this research is to analyze the employee performance associated with organization's growth. The research starts with collecting the employee information and then a deep exploratory data analysis has been conducted on the collected data to identify the significance contribution towards the organization. Finally, ensemble learning framework designed to identify the potential of employees to the organization. To the best of our knowledge, this is the first work which consists of all the aspects of employee performance has been included in the dataset. Also, we have used the real time employee dataset from reputed MNC Company from Chennai. The results show that the proposed Extreme gradient boosting ensemble learning algorithm gives better results.
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