Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach

MOTIVATION: Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the samples into at least training and test set...

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Detalles Bibliográficos
Autores principales: Morais, Camilo L M, Santos, Marfran C D, Lima, Kássio M G, Martin, Francis L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954661/
https://www.ncbi.nlm.nih.gov/pubmed/31116391
http://dx.doi.org/10.1093/bioinformatics/btz421

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954661/
https://www.ncbi.nlm.nih.gov/pubmed/31116391
http://dx.doi.org/10.1093/bioinformatics/btz421

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