Ensemble Selection

Ensemble selection aims to identify the optimal subset of machine learning models from a larger pool to maximize predictive accuracy and efficiency. Current research focuses on developing efficient algorithms, including meta-learning approaches for recommending optimal selection strategies and methods that incorporate hardware constraints or data characteristics like class imbalance and concept drift. These advancements are significant for improving the performance and resource efficiency of automated machine learning systems, with applications ranging from healthcare diagnostics to resource-constrained deployments.