I am trying to build an ensemble model to classify dataset with imbalanced data, where some of classes have just a few samples. And, because of this dataset property, when I am doing re-sampling with replacement, some of classes become "discarded", i.e. there is no samples belonging to these small-sized classes in bootstrapped dataset.
Is there any solution to this issue except original's dataset augmentation/extending? What if I just change bootstrapping process to make sure that all classes are included into bootstrapped dataset? Could it somehow affect the learning process?
Or do you think that in this case one cannot apply bootstrapping at all and should to increase number of samples per class at first?