Li Wenhui
School of Information Science and Engineering, Shandong Normal University, Shandong, China.
PeerJ Comput Sci. 2023 Sep 27;9:e1570. doi: 10.7717/peerj-cs.1570. eCollection 2023.
In all areas of business, employee attrition has a detrimental impact on the accuracy of profit management. With modern advanced computing technology, it is possible to construct a model for predicting employee attrition to minimize business owners' costs. Despite the reality that these types of models have never been evaluated under real-world conditions, several implementations were developed and applied to the IBM HR Employee Attrition dataset to evaluate how these models may be incorporated into a decision support system and their effect on strategic decisions. In this study, a Transformer-based neural network was implemented and was characterized by contextual embeddings adapting to tubular data as a computational technique for determining employee turnover. Experimental outcomes showed that this model had significantly improved prediction efficiency compared to other state-of-the-art models. In addition, this study pointed out that deep learning, in general, and Transformer-based networks, in particular, are promising for dealing with tabular and unbalanced data.
在商业的各个领域，员工流失对利润管理的准确性都有不利影响。借助现代先进的计算技术，可以构建一个预测员工流失的模型，以尽量减少企业主的成本。尽管这类模型从未在实际条件下进行过评估，但还是开发了几种模型并将其应用于IBM人力资源员工流失数据集，以评估这些模型如何能够纳入决策支持系统以及它们对战略决策的影响。在本研究中，实现了一个基于Transformer的神经网络，其特点是通过上下文嵌入来适应管状数据，作为一种确定员工流动率的计算技术。实验结果表明，与其他最先进的模型相比，该模型的预测效率有了显著提高。此外，本研究指出，一般而言深度学习，特别是基于Transformer的网络，在处理表格数据和不平衡数据方面很有前景。
