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Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values. While feature selection approaches and imputation techniques address...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8628399/ https://www.ncbi.nlm.nih.gov/pubmed/34844620 http://dx.doi.org/10.1186/s13040-021-00283-6 |