Feature selection in HR analytics: A hybrid optimization approach with PSO and GSO

Abstract

In the field of Human Resources (HR) analytics, effective feature selection is critical for improving the accuracy and efficiency of predictive models used for workforce management, talent retention, and performance evaluation. This paper proposes an improved feature selection approach that integrates optimization techniques such as particle swarm optimization (PSO) and gravitational search optimization (GSO) to enhance the performance of HR analytics. By leveraging the exploration-exploitation balance of PSO and the mass-based search capability of GSO, the proposed method efficiently identifies the most relevant features from large and complex HR datasets. The hybrid approach reduces dimensionality, minimizes computational costs, and boosts the accuracy of machine learning models used in HR analytics. Comparative analysis with traditional feature selection methods demonstrates that the proposed technique achieves superior results in terms of prediction accuracy, computational efficiency, and overall model performance. This study highlights the potential of advanced optimization techniques in driving data-driven decision-making processes in HR, offering a robust and scalable solution for managing and analyzing HR data more effectively.