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Synthetic minority oversampling of vital statistics data with generative adversarial networks
OBJECTIVE: Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-typ...
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| 出版年: | J Am Med Inform Assoc |
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| 主要な著者: | , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Oxford University Press
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7750982/ https://ncbi.nlm.nih.gov/pubmed/32885818 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/jamia/ocaa127 |
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