Application of Support Vector Machines in Predicting Employee Turnover Based on Job Performance

Abstract

Accurate employee turnover prediction plays an important role in providing early information for unanticipated turnover. A novel classification technique, support vector machines (SVMs), has been successfully employed in many fields to deal with classification problems. However, the application of SVMs for employee voluntary turnover prediction has not been widely explored. Therefore, this investigation attempts to examine the feasibility of SVMs in predicting employee turnover. Besides, two other tradition regression models, Logistic and Probability models are used to compare the prediction accuracy with the SVM model. Subsequently, a numerical example of employee voluntary turnover data from a middle motor marketing enterprise in central Taiwan is used to compare the performance of three models. Empirical results reveal that the SVM model outperforms the logit and probit models in predicting the employee turnover based on job performance. Consequently, the SVM model is a promising alternative for predicting employee turnover in human resource management.