I have data that is split into three classes (A, B and noise). The data amount is around 10000 samples, and A and B is only less than 5-10% of data. What is the best approach to handle this situation and what ML methods are most suitable for it?
The misclassification of noise (treating noise as A or B) and treating A as B (or vice versa) is more important to avoid. Treating A or B as noise is not so important error unless we have some correct A and classifications.
In this case, I'm also thinking of making multistage system (first filter noise using binary classification) and then classify data into A and B classes. Will this approach be more useful?