I have a data set that has 660,000 samples with 72 features and I'm trying to perform feature selection so that I can train a naive bayes classifier. The problem is that since the data set is so big, I can't process the entire file without my computer freezing up. I originally planed on performing feature ranking with information gain by just taking a subsample of the data. The problem is that each time I run my program, I get a different order for the features.
I'm trying to figure out, how large of a percentage of the training data do i need to get an accurate measure for my information gain?