Open Access

ARTICLE

Envisaging Employee Churn Using MCDM and Machine Learning

by Meenu Chaudhary¹, Loveleen Gaur¹, NZ Jhanjhi^2,*, 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:

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

Received 07 September 2021; Accepted 18 November 2021; Issue published 08 February 2022

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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Keywords

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