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Learning to improve medical decision making from imbalanced data without a priori cost
BACKGROUND: In a medical data set, data are commonly composed of a minority (positive or abnormal) group and a majority (negative or normal) group and the cost of misclassifying a minority sample as a majority sample is highly expensive. This is the so-called imbalanced classification problem. The t...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2014
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4261533/ https://www.ncbi.nlm.nih.gov/pubmed/25480146 http://dx.doi.org/10.1186/s12911-014-0111-9 |