A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.
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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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