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A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.
Cusworth, Samuel; Gkoutos, Georgios V; Acharjee, Animesh.
Afiliación
  • Cusworth S; Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
  • Gkoutos GV; NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK.
  • Acharjee A; College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
BMC Med Inform Decis Mak ; 24(1): 90, 2024 Mar 28.
Article en En | MEDLINE | ID: mdl-38549123
ABSTRACT
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Aprendizaje Automático Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Aprendizaje Automático Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article