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InvariantSVM

Supplementary files for "Constraining classifiers in molecular analysis: invariance and robustness"

Readme

ReadMe file for the supplementary material.

ReadMe

Code

This provides the code used for training the (invariant) SVMs and predicting the class labels of a new matrix in R.

R-Package: invariantSVM

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 = 2
library(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))))

Datasets

Collection of the analysed artificial datasets.

Artificial dataset

Artificial dataset (with noise)

Fold lists

Collection of the fold lists used for the 10x10 cross-validation experiments.

Fold-Lists (real datasets)

Result files

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.

Result tables

Latest News

 

Our paper "Assessing phenotype order in molecular data" has been published in Scientific Reports.

 

Our paper "Representing dynamic biological networks with multi-scale probabilistic models"has been published in Communications Biology.