Bias-corrected Bayesian Classification with Selected Features
Longhai Li, Department of
Mathematics and Statistics, University of Saskatchewan
Copyright
Permission is granted for anyone to copy, use, modify, or distribute
these programs and accompanying documents for any purpose, provided
this copyright notice is retained and prominently displayed, and note
is made of any changes made to these programs. These programs and
documents are distributed without any warranty, express or implied. As
the programs were written for research purposes only, they have not
been tested to the degree that would be advisable in any important
application. All use of these programs is entirely at the user's own
risk.
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.
Source Packages and Documentations
References
The methods used in this software are discussed in details in the
following papers:
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