Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
Comput Intell Neurosci. 2022 Jun 26;2022:7728668. doi: 10.1155/2022/7728668. eCollection 2022.
Competent employees are a rare commodity for great companies. The problem of maintaining good employees with experience threatens the owners of companies. The issue of employee attrition can cost employers a lot as it takes a lot to compensate for their expertise and efficiency. For this reason, in this research, we present an automated model that can predict employee attrition based on different predictive analytical techniques. These techniques have been applied with different pipeline architectures to select the best champion model. Also, an autotuning approach has been implemented to calculate the best combination of hyper parameters to build the champion model. Finally, we propose an ensemble model for selecting the most efficient model subject to different assessments measures. The results of the proposed model show that no model up until now could be considered ideal and perfect for each case of business context. Yet, our chosen model was pretty much optimal as per our requirements and adequately satisfied the intended goal.
对于优秀的公司来说，有能力的员工是稀缺资源。拥有经验的优秀员工流失的问题，给公司老板带来了威胁。员工离职的问题可能会给雇主造成很大的损失，因为要弥补他们的专业知识和效率需要花费很多。出于这个原因，在这项研究中，我们提出了一个自动化的模型，可以基于不同的预测分析技术来预测员工流失。这些技术已经应用于不同的管道架构，以选择最佳的冠军模型。此外，还实现了自动调整方法来计算构建冠军模型的最佳超参数组合。最后，我们提出了一个集成模型，用于根据不同的评估指标选择最高效的模型。所提出模型的结果表明，到目前为止，还没有一种模型可以被认为是适用于每种业务环境的理想和完美的模型。然而，我们选择的模型根据我们的要求是非常理想的，并且充分满足了预期的目标。
