This study presents a novel Optimised Parallelised Ensemble Learning (OPEL) framework that enhances multi-ensemble learning through a unique combination of Parallel multi-Model Execution, Consensus-Based Model Selection (CMS), and an Optimised Parallel Voting Mechanism. Together, these components significantly reduce computational complexity, as analytically supported by Amdahl's Law, while enhancing model robustness by dynamically varying participating voting models for any varied sample sizes through model selection, weighting, and parallel execution strategies. Performance metrics utilised selected top-performing models, achieving speed-ups of up to 1.3 ms for some samples and higher accuracy scores. These results validate OPEL as a scalable, efficient, and high-performing approach for ensemble learning in resource-constrained and high-throughput applications. Unlike existing methods such as Auto-ML or A-Stacking, OPEL's real-time dynamic model selection and multi-model parallel execution significantly show improved accuracy. Experimental simulations on real-world datasets demonstrated significant improvement of around 5.6% in model accuracy on weather-based sales prediction datasets and had a win rate of 60.64%, unlike Auto-ML for the hotel booking predictions, using McNemar's analysis. A paired t -test confirmed the statistical significances of these improvements, proving OPEL to be a scalable, adaptive ensemble framework for real-time applications that demanded both speed and accuracy by selecting and re-weighting models dynamically during runtime based on live performance metrics, offering dynamic and computationally efficient system as compared to traditional methods, validated across classification tasks involving SME market sales and hotel booking datasets. OPEL's novel contribution lies in its run-time optimised voting and parallel selection mechanism, making it suitable for dynamic-non-stationary environments.
Pelekamoyo et al. (Wed,) studied this question.