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Envisaging Employee Churn Using MCDM and Machine Learning

Meenu Chaudhary1, Loveleen Gaur1, NZ Jhanjhi2,*, Mehedi Masud3, Sultan Aljahdali3

1 Amity International Business School, Amity University, Noida, 201301, India
2 School of Computer Science and Engineering SCE, Taylor's University, Subang Jaya, 47500, Malaysia
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: NZ Jhanjhi. Email: email

Intelligent Automation & Soft Computing 2022, 33(2), 1009-1024. https://doi.org/10.32604/iasc.2022.023417

Abstract

Employee categorisation differentiates valuable employees as eighty per cent of profit comes from twenty per cent of employees. Also, retention of all employees is quite challenging and incur a cost. Previous studies have focused on employee churn analysis using various machine learning algorithms but have missed the categorisation of an employee based on accomplishments. This paper provides an approach of categorising employees to quantify the importance of the employees using multi-criteria decision making (MCDM) techniques, i.e., criteria importance through inter-criteria correlation (CRITIC) to assign relative weights to employee accomplishments and fuzzy Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) method to divide employees into three categories. Followed by executing churn analysis of each category of employees and original dataset using machine learning algorithms to investigate the importance of employee categorisation. CatBoost, Support Vector Machine, Decision Tree, Random Forest and XGradient Boost algorithms have been used to analyse the categorised and non-categorised dataset on the accuracy, precision, recall and Mathew's Correlation Coefficient (MCC) to derive the best suitable algorithm for the used dataset. CatBoost algorithm showed the best results regarding performance measurements for categorised employees are better than all employee datasets.

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APA Style
Chaudhary, M., Gaur, L., Jhanjhi, N., Masud, M., Aljahdali, S. (2022). Envisaging employee churn using MCDM and machine learning. Intelligent Automation & Soft Computing, 33(2), 1009-1024. https://doi.org/10.32604/iasc.2022.023417
Vancouver Style
Chaudhary M, Gaur L, Jhanjhi N, Masud M, Aljahdali S. Envisaging employee churn using MCDM and machine learning. Intell Automat Soft Comput . 2022;33(2):1009-1024 https://doi.org/10.32604/iasc.2022.023417
IEEE Style
M. Chaudhary, L. Gaur, N. Jhanjhi, M. Masud, and S. Aljahdali "Envisaging Employee Churn Using MCDM and Machine Learning," Intell. Automat. Soft Comput. , vol. 33, no. 2, pp. 1009-1024. 2022. https://doi.org/10.32604/iasc.2022.023417



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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