Employee Turnover Prediction based on Particle Swarm Optimization - Support Vector Machine

Abstract

Employee turnover is getting more and more attention in the human resources field. Unexpected turnover of employees is blamed for the loss of work handover. As a result, predicting whether employees would leave has become a crucial problem. This research aims to exploit a method combining particle swarm optimization with a support vector machine to address the employee departure prediction problem. In this study, the particle swarm optimization algorithm is used to optimize the parameter selection of the support vector machine to improve the performance of the latter. Moreover, employee information of a dataset is subject to correlation analysis before being transformed into standardization form to accelerate convergence and improve the accuracy of the support vector machine. Eventually, the support vector machine combined with particle swarm optimization is of best performance in accuracy score, precision score and F1 score, respectively reaching 0.873, 0.947 and 0.784. In conclusion, this method addresses the employee turnover prediction problem effectively which also provides a new direction for applying swarm intelligence algorithms.