Feature selection for distance-based regression: An umbrella review and a one-shot wrapper

Feature selection (FS) may improve the performance, cost-efficiency, and understandability of supervised machine learning models. In this paper, FS for recently introduced distance-based model is considered regression problems. The study contextualized by first providing an umbrella review (review reviews) recent development in research field. We then propose a saliency-based one-shot wrapper algorithm FS, which called MAS-FS. compared with set other popular algorithms, using versatile simulated benchmark datasets. Finally, experimental results underline usefulness regression, confirming utility certain filter algorithms particularly proposed algorithm.