https://www.svds.com/learning-imbalanced-classes/ explains quite nicely the different ways to handle an imbalanced dataset. But there is an information under the random undesampling and random oversampling technique which I am not sure is correct or not, as I could not find the same information in different research articles (paper1,paper2). The information for which I will highly appreciate clarifications are:
1) Does random oversampling the minority class increase the size of the final data set such that each class is the same size as that of the majority?
2) Does random undersampling decrease the total size of the dataset such that each class is the same size as that of the minority?
For example, if the minority class has 20 examples and majority class has 80 examples, then would the result of random oversampling be: (20+80) + 80 = 180
and for the random undersampling technique: 20 + (80-60) = 20+20 =40?
3) Are these methods random sampling with or without replacement?