Employee Classification in Reward Allocation Using ML Algorithms

Abstract

This work discussed an application of machine learning algorithms in predicting employee categories in reward allocation based on input features determined from survey responses. The results reported in this article are primarily based on beliefs and perceptions of the survey respondents about the four categories of employees, namely performer, needy, starter, and senior. The authors considered two classification models—full model with 10 input features and the reduced model with seven input features—and the results show that the reduced model performed better than the full model, indicating that three qualitative input features bear no relevance to predicting the employee categories. Both models selected optimizable ensemble and optimizable SVM as best machine learning classifiers, based on accuracy rates and AUC scores. Finally, using the reduced model on out-of-sample observations, employee categories were correctly predicted matching the actual categories.