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 of supervised approaches. In these cases, semi-supervised learning algorithms can be a way to circumvent the problem.

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