Bagging for Imbalanced dataset
I have a very imbalanced dataset (t: 14% , f:86%) , I want to use bagging in a way that I can sample roughly 1/3 of f class and union it with true class and train naive bayes on it ,
I have two question :
1.How can I do this kind of sampling (like what happens in bagging tool in rapid miner but not sampling the whole dataset but only the major class)
2.what type of naive bayes do you suggest me to use inside baggin ? because there are different implementation of various types of naive bayes in rapidminer ? should it be reweightable ? should it be updateable ?