I'm working on Imbalance Classification problem with minority class(0.017%).
I've read that imbalance classification can be handled using Undersampling, Oversampling and SMOTE.
Major drawback of Undersampling is we ignore lot of information and the results with SMOTE are slightly better.
But my questions are
SMOTE also does undersampling of majority class and how the results produced by SMOTE are better?
SMOTE(form, data, perc.over = 200, k = 5, perc.under = 200, learner = NULL, ...)
A number that drives the decision of how many extra cases from the majority classes are selected for each case generated from the minority class (known as under-sampling
What sort of classification problems where Undersampling/Oversampling produces better results than SMOTE?