CMA-package: Synthesis of microarray-based classification
In chbernau/CMA: Synthesis of microarray-based classification

Description Details Author(s) References

The aim of the package is to provide a user-friendly environment for the evaluation of classification methods using gene expression data. A strong focus is on combined variable selection, hyperparameter tuning, evaluation, visualization and comparison of (up to now) 21 classification methods from three main fields: Discriminant Analysis, Neural Networks and Machine Learning. Although the package has been created with the intention to be used for Microarray data, it can as well be used in various (p > n)-scenarios.

Package:	CMA
Type:	Package
Version:	1.3.3
Date:	2009-9-14
License:	GPL (version 2 or later)

Most Important Steps for the workflow are:

1.: Generate evaluation datasets using GenerateLearningsets
2.: (Optionally): Perform variable selection using GeneSelection
3.: (Optionally): Peform hyperparameter tuning using tune
4.: Perform classification using 1.-3.
5.: Repeat 2.-4. based on 1. for several methods: compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA
6.: Evaluate the results from 5. using evaluation and make a comparison by calling compare

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

Christoph Bernau bernau@ibe.med.uni-muenchen.de

Maintainer: Christoph Bernau bernau@ibe.med.uni-muenchen.de.

Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439

chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.