Adaptive Multi-Layer Ensemble Learning (AMEL) for Robust and Accurate Predictive Modelling in Big Data Analytics

The rapid expansion of data in various domains necessitates advanced analytics techniques to derive meaningful insights and make informed decisions. This paper presents a novel approach named Adaptive Multi-Layer Ensemble Learning (AMEL) for improving the accuracy and robustness of predictive models in big data analytics. AMEL leverages a multi-layer architecture that dynamically integrates diverse machine learning algorithms through adaptive weighting mechanisms to address issues of model bias and variance. Our approach is evaluated on three large-scale datasets from healthcare, finance, and social media domains. The experimental results demonstrate that AMEL achieves a 5-10% improvement in prediction accuracy compared to traditional ensemble methods such as Bagging and Boosting. Furthermore, AMEL exhibits enhanced stability and robustness in the presence of noisy and imbalanced data, as evidenced by a 15% reduction in prediction variance and a 20% improvement in F1 scores on average across the datasets. The proposed AMEL framework not only advances the state-of-the-art in ensemble learning but also provides a scalable and adaptable solution for complex data analytics tasks. These findings underscore the potential of AMEL to significantly improve decision-making processes in various high-impact sectors.