A novel scheme for employee churn problem using multi-attribute decision making approach and machine learning

Employee churn (ECn) is a crucial problem for any organization that adversely affects its overall revenue and brand image. Many machine learning (ML) based systems have been developed to solve the ECn problem. However, they miss out on some essential issues such as employee categorization, category-wise churn prediction, and retention policy for effectively addressing the ECn problem. By considering all these issues, we propose, in this paper, a multi-attribute decision making (MADM) based scheme coupled with ML algorithms. The proposed scheme is referred as employee churn prediction and retention (ECPR). We first design an accomplishment-based employee importance model (AEIM) that utilizes a two-stage MADM approach for grouping the employees in various categories. Preliminarily, we formulate an improved version of the entropy weight method (IEWM) for assigning relative weights to the employee accomplishments. Then, we utilize the technique for order preference by similarity to ideal solution (TOPSIS) for quantifying the importance of the employees to perform their class-based categorization. The CatBoost algorithm is then applied for predicting class-wise employee churn. Finally, we propose a retention policy based on the prediction results and ranking of the features. The proposed ECPR scheme is tested on a benchmark dataset of the human resource information system (HRIS), and the results are compared with other ML algorithms using various performance metrics. We show that the system using the CatBoost algorithm outperforms other ML algorithms.

Automated summary

This paper proposes a scheme called Employee Churn Prediction and Retention (ECPR) based on multi-attribute decision making and machine learning algorithms. The scheme categorizes employees, predicts churn using the CatBoost algorithm, and proposes a retention policy based on prediction results and feature ranking. The ECPR scheme was tested on a benchmark HRIS dataset and compared with other ML algorithms, showing that CatBoost outperforms them.