Supplementary files for "Constraining classifiers in molecular analysis: invariance and robustness"
ReadMe file for the supplementary material.
This provides the code used for training the (invariant) SVMs and predicting the class labels of a new matrix in R.
The code is bundled as the R package invariantSVM_1.0.tar.gz. It can be installed from command line by using the command:
R CMD INSTALL invariantSVM_1.0.tar.gz
After the package is loaded into the workspace (by using library(invariantSVM)), automatic parameter tuning in a multi-objective setting can be performed as shown in the following example:
#calculate 10x10 Accuracy on iris dataset over all invariances and R = 2library(TunePareto)print(tunePareto(data = as.matrix(iris[1:100, -ncol(iris)]),labels = iris[1:100,ncol(iris)],classifier = tunePareto.invariantSVM(), C=1, R = 2,invariance = c("lin","off","con","off&con","mon"),objectiveFunctions = list(cvAccuracy(10, 10))))
Collection of the analysed artificial datasets.
Collection of the fold lists used for the 10x10 cross-validation experiments.
This file provides the results of the experiments on artificial and transcriptome datasets. The accuracies and the selected number of features (R1-SVM) are given.
Our paper "Constraining classifiers in molecular analysis: invariance and robustness" has been published in Journal of the Royal Society Interface.
Our paper "Measuring Mental Effort for Creating Mobile Data Collection Applications" has been published in International Journal of Environmental Research and Public Health.