M. El-Bashir

Meta-Learning for Dataset Selection: An Adaptive Utility Scoring Framework with Interpretable Meta-Feature Analysis

Dataset selection is commonly based on dataset size, prior experience, or computationally expensive trial-and-error evaluation. We treat that choice as a meta-learning problem instead. The Adaptive Dataset Utility Score (ADUS) ranks candidate datasets without training the downstream classifier portfolio on each candida...