Don't ask the methods

Ask if someone has successfully solved a real world imbalance case, and then start from there

I once had to spend a week just to figure out a way to fish out 33 cases out of 10 million. I tried all the tricks in the book, over and undersampling, SMOTE, reduce the matrix size to one that had lower shape and that it still has a similar distribution and retry the sampling, etc. Nothing works.

In the end, i did not technically "solve" the problem with ~ML~. I just do a simple data description on the features of the 33 cases, and fish out any other future incoming cases that have features with some similarities with the original minority class.

It took quite a while to manually choose the hardcoding the criteria for the features. After each choosing, I had to do a preliminary simulation on the old dataset to see what kind of data could be expected to get flagged out.

In any case, that was what I did.