Employee Promotion Prediction Using Improved AdaBoost Machine Learning Approach

Abstract

Employee promotion is an important aspect of the
human resource management process. Due to different factors, it
refers to the automatic improvement among the employees in an
organization. It improves their job satisfaction and motivation by
providing more significant income, status, and responsibilities. By
building up loyalty, promotion reduces employee attrition. But,
it is difficult to accurately decide, whether an employee should
or should not be promoted based on their current and past
performance. So, human resource management does research
about promotion prediction, because there are a limited number
of research about the finding of employee promotion prediction
in the existing studies. The aim of this research study is to
implement an employee promotion prediction framework using
machine learning. A modified AdaBoost classifier is used for automatic promotion prediction, and six machine learning techniques
like Support Vector Machine (SVM), Logistic Regression (LR),
Artificial Neural Network (ANN), Random Forest (RF), XGBoost
(XGB), and AdaBoost are applied in performance comparison.
Through a complex assessment process, the performance of
these supervised machine learning algorithms for predicting
employee advancement is analyzed using assessment metrics on
the employees’ evaluation dataset. The Artificial Neural Network
(ANN) and AdaBoost model provide better results on this dataset
than all traditional machine learning techniques. Finally, Our
proposed modified AdaBoost approach outperformed all other
methods evaluated with an accuracy of 95.30%