The obvious solution is to ensure that the training data is balanced - but in my particular case that is impossible. What corrections can one perform in such a scenario?
I know that my training data is heavily biased towards a particular class, say, and I cannot change that. Moreover, the labels are very noisy. Conditioned on this piece of information, is there anything I can do by tweaking the training process itself/ something else, to correct for the bias in the training data?
The data comes from an experiment (from an electron microscope), and I cannot collect more data. It's always going to be biased in this way, so alternatively-biased is also not an option. I'm sorry that I'm unable to provide any more details due to confidentiality.