MH-FSF: a Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation

Feature selection is vital for building effective predictive models, as it reduces dimensionality and emphasizes key features. However, current research often suffers from limited benchmarking and reliance on proprietary datasets. This severely hinders reproducibility and can negatively impact...