Boosting Accuracy: Advanced Ensemble Learning Strategies
Gagandeep Marken, Sk Mujibar Rahaman · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2025
Determining how to improve machine learning's predictive accuracy has led to notable developments in ensemble learning techniques.In order to solve prediction problems in a variety of datasets, this study, "Boosting Accuracy: Advanced Ensemble Learning Strategies," explores and applies cutting-edge boosting techniques.Three well-known methods are specifically the subject of this study: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machines (GBM).The advantages and disadvantages of these approaches are carefully assessed over a range of forecast scenarios.Our proposal is to create hybrid models that combine boosting techniques with other machine learning algorithms to create strong ensemble frameworks that go beyond the limits of conventional boosting approaches.The goal of this hybridisation is to get better predictive performance and robustness by utilising the advantages of each distinct model.