6533b872fe1ef96bd12d3901

RESEARCH PRODUCT

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

Joakim LinjaJoonas HämäläinenPaavo NieminenTommi Kärkkäinen

subject

EMLMfeature selectionkoneoppiminenArtificial IntelligenceCognitive Neurosciencealgoritmitparantaminen (paremmaksi muuttaminen)tekoälydistance-based methodwrapper algorithmfeature saliencyComputer Science Applications

description

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

yearjournalcountryeditionlanguage
2023-01-01Neurocomputing
https://doi.org/10.1016/j.neucom.2022.11.023