Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features...

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Detalles Bibliográficos
Autores principales: Abed-alguni, Bilal H., Alawad, Noor Aldeen, Al-Betar, Mohammed Azmi, Paul, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547101/
https://www.ncbi.nlm.nih.gov/pubmed/36247211
http://dx.doi.org/10.1007/s10489-022-04201-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547101/
https://www.ncbi.nlm.nih.gov/pubmed/36247211
http://dx.doi.org/10.1007/s10489-022-04201-z

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