
test_automated_feature_selection.m - Evaluate different feature selection techniques, such as Relieff.
test_normalization_freesurfer.m - Demonstration of the data harmonization procedure with Freesurfer data.
test_adnimerge.m - Demonstration of the data harmonization procedure with ADNIMERGE data.

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class_learn_app_auto - Main function used to evaluate the different classification methods using different feature selection techniques.
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Handles for the different classification functions:
train_GaussNaiveBayes.m
train_KernelNaiveBayes.m
train_KNN_BayesianOptim.m
train_KSVM_BayesianOptim.m
train_LogisticReg_BayesianOptim.m
train_RussBoost_BayesianOptim.m
train_SVMReg_BayesianOptim.m

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Code for creation of Figures 5 and 6
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run_fig5 - Run the main computations to calculate measures for the figures
honcv - Implements nested cross-validation within a Monte Carlo replication analysis
plot_fig5 - Plot the figures as illustration
prepare_SPSS_1stPaper_imbalanced - pool together all the outcome in Excell files for the ANOVA and other statistical analyses
