I have data currently available that is very accurate and I would like to train my classification methods on this set of clean data to learn the important markers for distinguishing between classes. But in the future, my trained classifiers will not be seeing and performing decisions on this cleaned data; instead, it will likely have a lot more noise following some unknown distribution(s). Thus I am wondering, is it 'better' to train on noisy data if I'm going to likely see noisy data in the future, or train on good data since the noisy data should (ideally) correspond to the cleaned data if noise was removed?
Intuitively, if my goal is to simply perform classifications, then training on noisy data seems the 'better' approach since this is better representative of my expected future inputs. But if my goal is to learn about the data and what constitutes a particular decision, then training on cleaned data appears the 'better' approach.
But am I overlooking anything? Would training on the clean and/or noisy data be preferable for different reasons (e.g. generalization, overfitting, reducing dimensionality)?