Bias-corrected Bayesian Classification with Selected Features
Description
This software is used to predict the discrete class labels
based on a selected subset of high-dimensional features, such
as expression levels of genes. The data are modeled with a
hierarchical Bayesian models using heavy-tailed t distributions
as priors. When a large number of features are available, one
may like to select only a subset of features to use, typically
those features strongly correlated with the response in
training cases. Such a feature selection procedure is however
invalid since the relationship between the response and the
features has be exaggerated by feature selection. This package
provides a way to avoid this bias and yield better-calibrated
predictions for future cases when one uses F-statistic to
select features.