A novel deep learning model based on convolutional neural networks for employee churn prediction

Ebru Pekel Ozmen*, Tuncay Ozcan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

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 Nowadays, traditional classification algorithms have been replaced by deep learning models.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.

Original languageEnglish
Pages (from-to)539-550
Number of pages12
JournalJournal of Forecasting
Volume41
Issue number3
DOIs
Publication statusPublished - Apr 2022

Bibliographical note

Publisher Copyright:
© 2021 John Wiley & Sons, Ltd.

Funding

We would like to thank the reviewers for their thoughtful comments and efforts toward improving our manuscript.

Keywords

  • classification and regression tree
  • CNN
  • deep learning
  • employee churn
  • retailing

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