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.
Package details |
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Author | Longhai Li <longhai@math.usask.ca> |
Maintainer | Longhai Li <longhai@math.usask.ca> |
License | GPL (>= 2) |
Version | 1.0-1 |
URL | http://www.r-project.org http://math.usask.ca/~longhai |
Package repository | View on CRAN |
Installation |
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