  Prediction of survival for cancer patients is an open area of research.
However, many of these studies focus on datasets with a large number of
patients. We present a novel method that is specifically designed to address
the challenge of data scarcity, which is often the case for cancer datasets.
Our method is able to use unlabeled data to improve classification by adopting
a semi-supervised training approach to learn an ensemble classifier. The
results of applying our method to three cancer datasets show the promise of
semi-supervised learning for prediction of cancer survival.
