The dataset consists of Eeg electrode power features in all power bands(alpha, beta, delta..both relative and absolute) and source power features (obtained after source estimation) in addition to connectivity strengths between the different sources (brain regions). There are as many as 20k features in all.
If i have to predict disease(binary classifier) based on all above features, what approach will yield best accuracy on test sets? I am wondering if i must fit seperate classifiers for each type of feature set (after dimension reduction) and then use the output probabilities obtained to write a meta classifier on top to predict the final disease state.
I am looking for a good discussion on possible approaches and/or a sample solution. Thank you.