Stacking Ensemble

Stacking ensemble methods combine predictions from multiple base machine learning models to improve overall predictive accuracy and robustness. Current research focuses on optimizing stacking architectures, including the selection and weighting of base learners (e.g., using transformers, support vector machines, or neural networks like CNNs and LSTMs), and exploring different meta-learners (e.g., Bayesian regression, linear models). This approach finds applications across diverse fields, from medical diagnosis (e.g., breast cancer classification) and financial forecasting (e.g., customer lifetime value prediction) to anomaly detection in industrial control systems, demonstrating its broad utility and impact on various scientific and practical problems.