Instance Selection

Instance selection focuses on optimizing machine learning model training by carefully choosing a subset of the available data, aiming to improve model performance, efficiency, and generalization. Current research explores instance selection within various contexts, including reinforcement learning for dynamic algorithm configuration, improving the robustness of interactive segmentation models for medical image analysis, and mitigating data sparsity in electronic health records. These advancements have significant implications for diverse fields, ranging from medical diagnostics and personalized medicine to efficient algorithm design and anomaly detection in large datasets.