Semi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-Effect

Supervised learning algorithms have been widely used as predictors and applied in a myriad of studies. The accuracy of the classification algorithms is strongly dependent on the existence of large and balanced training sets. The existence of a reduced number of labeled data can deeply affect the use...

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Detalhes bibliográficos
Autor principal: Soares, Inês (author)
Outros Autores: Dias, Joana (author), Rocha, Humberto (author), Khouri, Leila (author), Lopes, Maria do Carmo (author), Costa Ferreira, Brigida (author)
Formato: bookPart
Idioma:eng
Publicado em: 2021
Assuntos:
Radiotherapy
Xerostomia
Semi-supervised learning
Small databases
Unbalanced datasets
Texto completo:http://hdl.handle.net/10400.22/17493
País:Portugal
Oai:oai:recipp.ipp.pt:10400.22/17493
Descrição
Resumo:Supervised learning algorithms have been widely used as predictors and applied in a myriad of studies. The accuracy of the classification algorithms is strongly dependent on the existence of large and balanced training sets. The existence of a reduced number of labeled data can deeply affect the use of supervised approaches. In these cases, semi-supervised learning algorithms can be a way to circumvent the problem.

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