Geoffrey J. McLachlan

1 Introduction Classifiers such as neural networks often achieve their strong performance through a completely supervised learning approach, which requires a fully classified (labelled) dataset. However, missing labels often occur in practice because of the difficulty determining the true label for an observation (the feature vector). For example, in medicine and defence, images can often be correctly classified only by a limited number of experts in the field.

Abstract In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate.