IMPROVING PREDICTIVE ACCURACY THROUGH ENSEMBLE LEARNING TECHNIQUES IN DYNAMIC DATA ENVIRONMENTS
Keywords:
Ensemble Learning, Predictive Accuracy, Dynamic Environments, Bagging, Boosting, Stacking, Adaptive ModelsSynopsis
Purpose: The aim of this paper is to explore how ensemble learning techniques enhance predictive accuracy in dynamic data environments, where data distributions evolve over time. Design/methodology/approach: This work reviews existing literature on ensemble methods such as bagging, boosting, and stacking, and proposes a conceptual framework for applying these methods to dynamically evolving data. Findings: Ensemble techniques consistently improve prediction accuracy over single models by reducing bias and variance and increasing robustness in varying data conditions. Practical implications: Practitioners can adopt ensemble frameworks to improve model reliability in fields such as real time forecasting, adaptive systems, and streaming data contexts. Originality/value: This paper synthesizes key ensemble methods and aligns them with dynamic data challenges, emphasizing adaptive strategies for evolving environments.
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