Boosting Ensemble

Boosting ensembles combine multiple weak learning models to create a powerful predictive model, aiming to improve accuracy and robustness compared to individual models. Current research focuses on enhancing ensemble performance through techniques like co-boosting, which iteratively improves both the ensemble and its training data, and exploring various boosting algorithms (e.g., AdaBoost, Gradient Boosting, XGBoost, CatBoost) across diverse applications. These advancements are impacting fields ranging from medical diagnosis (e.g., PCOS prediction) to time series forecasting and even federated learning, demonstrating the broad applicability and significant potential of boosting ensembles for solving complex prediction problems.