Fully Bayesian Classification with a 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.
|Author||Longhai Li <firstname.lastname@example.org>|
|Date of publication||2015-09-26 01:05:27|
|Maintainer||Longhai Li <email@example.com>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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