Classification rule based on Bayesian mixture models with feature selection bias corrected

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.

If you found that this software is useful in your work, please do not hesitate to cite the software.

Description

This R package is used to predict the binary response based on high dimensional binary features modeled with Bayesian mixture models. The model is trained with Gibbs sampling. A smaller number of features can be selected based on the correlations with the response. The bias due to the selection procedure can be corrected. The software is written entirely with R language.

The software is most suitable for analyzing the data with very high dimension, for example the diagnosis of cancer based on the gene expression data.

Source Packages and Documentations

  1. predmixcor_1.1-1, released 21/02/2008, Documentation: predmixcor_1.1-1.pdf, source package: predmixcor_1.1-1.tar.gz

References

  • Li, L. (2007), Bayesian Classification and Regression with High Dimensional Features, Ph.D. thesis, University of Toronto: abstract

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