View article

Class prediction based on high-dimensional features has received a great deal of attention in many areas of application. For example, biologists are interested in using microarray gene expression profiles for diagnosis or prognosis of a certain disease (e.g., cancer). For computational and other reasons, it is necessary to select a subset of features before fitting a statistical model, by evaluating how strongly the features are related to the response. However, such a feature selection procedure will result in overconfident predictive probabilities for future cases, because the signal-to-noise ratio in the retained features is exacerbated by the feature selection. In this article we develop a hierarchical Bayesian classification method that can correct for this feature selection bias. Our method, which we term bias-corrected Bayesian classification with selected features (BCBCSF), uses the partial information from the feature …