Use multiple feature selection algorithms to derive robust feature sets for two or multiclass classification problems.


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Documentation for package ‘bootfs’ version 1.4.3

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bootfs-package Use multiple feature selection algorithms to derive robust feature sets for two class classification problems.
bootFS Use multiple feature selection algorithms to derive robust feature sets for two class classification problems.
bootfs Use multiple feature selection algorithms to derive robust feature sets for two class classification problems.
bsGBM Wrapper for GBM bootstrapping
bsPAMR Perform PAMR bootstrapping.
bsRFBORUTA Perform RFBORUTA bootstrapping.
bsSCAD Perform SCAD SVM bootstrapping.
control_params Create control parameter object for the classifiers
cvGBM Make a crossvalidation using GBM.
cvPAMR Main wrapper to call PAMR crossvalidation.
cvRFBORUTA Crossvalidation for Random Forests with Boruta feature selection.
cvSCAD Crossvalidation for SCAD SVM classification and feature selection.
cv_gbmclass Internal crossvalidation method for GBM classification
doBS Perform bootstrapped feature selection with multiple algorithms.
doCV Performance evaluation by crossvalidation for multiple classification algorithms.
drawheat Wrapper for heatmap drawing.
extractsignatures Helper for extracting all feature signatures from a bootstrapping result (single method).
fitGBM Fit a Gradient Boosting Machine model.
gbm_multi Calling function to GBM bootstrapping
importance_igraph Graphically represent the (co-)occurrences of a set of features, derived in a bootstrapped feature selection.
makeIG Create an importance graph from a bootstrapping result of a single classification method.
resultBS Summarise the results of a bootstrapping analysis.
resultCV create a result plot for all performed crossvalidations
simDataSet simDataSet - simulation of exemplary dataset