Ranks underlie outcome of combining classifiers: Quantitative roles for diversity and accuracy

Combining classifier systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Classification most commonly improves when the classifiers are “sufficiently good” (generalized as “accuracy”) and “sufficiently different” (generalized as “diversity”), but the ind...

Descripción completa

Detalles Bibliográficos
Autores principales: Sniatynski, Matthew J., Shepherd, John A., Ernst, Thomas, Wilkens, Lynne R., Hsu, D. Frank, Kristal, Bruce S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8848007/
https://www.ncbi.nlm.nih.gov/pubmed/35199065
http://dx.doi.org/10.1016/j.patter.2021.100415

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8848007/
https://www.ncbi.nlm.nih.gov/pubmed/35199065
http://dx.doi.org/10.1016/j.patter.2021.100415

Ejemplares similares