I have a problem where I need to classify something around 50 different classes. Some of the classes are very similar to one another and the algorithm tends to confuse them. However, I can create a tailored synthetic dataset for my problem and I am now wondering if I can tackle the hard cases by tweaking the distribution in training data, which seems a bit simpler than tweaking the loss function.
Normally, I would create a dataset with uniform distribution for all the classes to avoid all bias. But could artificially oversampling the classes that are easily confused lead to improved performance? Could you point me to resources about this?
Note this is not about classifying imbalanced data, which most of the papers related to oversampling seem to be tackling. My test data is rather balanced.