A Long Short-Term Memory with Recurrent Neural Network and Brownian Motion Butterfly Optimization for Employee Attrition Prediction

Abstract

Machine learning is a popular technology that continuously supports organizations in their corporate strategies, managerial functions, and team building. There are many areas in which organizations can adopt technologies that will support decision-making. Human resources (HR) have received more attention in recent years as a result of the fact that skilled employees are a significant growth factor and a genuine competitive advantage for the business. Machine learning started to be employed in HR management to help with employee-related decisions after first being introduced to the departments of sales and marketing. The goal is to support decisions that are based on objective analysis of data rather than on subjective factors. In this research, a novel long short-term memory with recurrent neural network (LSTM-RNN) method is implemented for the prediction and classification of employee attrition. To avoid the overfitting issue and make further prediction analysis based on the suitable objective of feature subset selection, the feature selection algorithm termed brownian motion based butterfly optimization algorithm is proposed. The presented model is validated on the international business machines corporation (IBM) HR dataset which consists of 35 features of employees like business travel, age, education, department, daily rate, distance from home, etc. On the dataset of IBM HR, the proposed model achieved accuracy, recall, F score, and precision of 96.68%, 96.62%, 96.62%, 96.64% respectively. The outcomes proved that the proposed method provides better results in the classification of employee attrition.