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

Soares, Inês; Dias, Joana; Rocha, Humberto; Khouri, Leila; Lopes, Maria do Carmo; Costa Ferreira, Brigida

http://hdl.handle.net/10400.22/17493

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Authors

Lopes, Maria do Carmo

Costa Ferreira, Brigida

Abstract(s)

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.

Keywords

Radiotherapy Xerostomia Semi-supervised learning Small databases Unbalanced datasets

URI

http://hdl.handle.net/10400.22/17493

Citation

Soares I., Dias J., Rocha H., Khouri L., do Carmo Lopes M., Ferreira B. (2016) Semi-supervised Self-training Approaches in Small and Unbalanced Datasets: Application to Xerostomia Radiation Side-Effect. In: Kyriacou E., Christofides S., Pattichis C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_161