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• W3202054227 abstract "Employees are one of the most important resources of a company. The churn of valuable employees significantly affects a company's performance. The design of systems that predict employee churn is critical importance for companies. At this point, machine learning algorithms offer important opportunities for the diagnosis of employee churn. Nowadays, traditional classification algorithms have been replaced by deep learning models. In this study, firstly, a convolutional neural network (CNN) model was applied on a numerical data set for employee churn prediction in retailing. Later, because the data loss is too much in data transformations, a new hybrid extended convolutional decision tree model (ECDT) was proposed by improving the CNN algorithm. Finally, a novel model (ECDT-GRID) was developed by applying grid search optimization to improve the classification accuracy of ECDT. Numerical results showed that the developed ECDT-GRID model outperformed the CNN and ECDT models and basic classification algorithms in terms of classification accuracy, and this model provided an efficient methodology for prediction of employee churn." @default.
• W3202054227 created "2021-10-11" @default.
• W3202054227 creator A5009290438 @default.
• W3202054227 creator A5053420376 @default.
• W3202054227 date "2021-10-07" @default.
• W3202054227 modified "2023-10-10" @default.
• W3202054227 title "A novel deep learning model based on convolutional neural networks for employee churn prediction" @default.
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• W3202054227 doi "https://doi.org/10.1002/for.2827" @default.
• W3202054227 hasPublicationYear "2021" @default.
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