Citation :

G. Ramani, Lakshmi Praba V, "Improved Employee Attrition Forecasting with Attriboost: A Novel Hybrid Algorithm with Dynamic Feature Scoring," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 12, pp. 229-243, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I12P119

Abstract

Employee attrition remains a critical challenge for organizations, affecting productivity, team dynamics, and operational costs. Predicting employee turnover with high accuracy can help organizations proactively address retention issues and improve human resource strategies. This paper introduces AttriBoost, a novel hybrid machine learning algorithm that combines Adaptive Boosting (AdaBoost) with a dynamic feature selection mechanism for employee attrition prediction. The AttriBoost model improves prediction accuracy by dynamically adjusting feature importance based on their relevance at each iteration of the boosting process. The model begins by scoring and ranking features, followed by an iterative boosting procedure that emphasizes the most influential features. Through this adaptive mechanism, AttriBoost effectively handles imbalanced data and produces high-performance predictions tailored to diverse HR datasets. Experimental results demonstrate that AttriBoost outperforms traditional machine learning models, providing organizations with a powerful tool for recognising employees at risk of attrition. Furthermore, the model’s ability to offer interpretable insights into the key drivers of employee turnover makes it a valuable asset for HR professionals. The paper also discusses future research directions, including the integration of AttriBoost with real-time HR systems and its application to other HR-related challenges.

Keywords

Employee Attrition, Machine Learning, Predictive Analytics, Adaptive Boosting, Feature Selection, Employee Retention, Human Resource Analytics, AttriBoost, Workforce Planning, Data Science.

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