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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2021
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| Materias: | |
| 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 |