Commentary: The problem of class imbalance in biomedical data
- PMID: 32711988
- PMCID: PMC7769929
- DOI: 10.1016/j.jtcvs.2020.06.052
Commentary: The problem of class imbalance in biomedical data
Comment on
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Using machine learning to predict early readmission following esophagectomy.J Thorac Cardiovasc Surg. 2021 Jun;161(6):1926-1939.e8. doi: 10.1016/j.jtcvs.2020.04.172. Epub 2020 May 29. J Thorac Cardiovasc Surg. 2021. PMID: 32711985
References
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- Bao L, Juan C, Li J, Zhang Y. Boosted near-miss under-sampling on svm ensembles for concept detection in large-scale imbalanced datasets. Neurocomputing. 2016; 172:198–206.
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- Breiman L, Chen C, Liaw A. Using random forest to learn imbalanced data. Technical Report. University of California, Berkeley. 2004
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- Ishwaran H, Kogalur UB. Random forests for survival, regression and classification (rf-src). R package version 2.9.3 2020. Available at https://cran.r-project.org/package=randomForestSRC.
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