I am studying classification in imbalanced datasets and I am learning under/over sampling strategies as a way to address the issue. While the literature agrees one needs to oversample 'minority' classes and downsample 'majority' classes, I have not been able to find a clear definition of how minority/majority is defined/measured.
While this is not much an issue in a binary classification task, my problem is a multi-classification one, where there are over 200 classes, some have tens of thousands of examples some have under a hundred. But how do you scientifically decide which are majority, which are minority?
I'd appreciate some help on this, especially if you have any references that I can ready.