You are making several assumptions. It is best to think of the ultimate goal in general terms, then formulate a strategy that meets that goal. For example do you really need forced-choice classification and is the signal:noise ratio large enough to support that (good examples: sound and image recognition)? Or is the signal:noise ratio low or you are interested in tendencies? For the latter, risk estimation is for you. The choice is key and dictates the predictive accuracy metric you choose. For more thoughts on all this see http://www.fharrell.com/2017/01/classification-vs-prediction.html and http://www.fharrell.com/2017/03/damage-caused-by-classification.html.
The majority of problems concern decision making, and optimum decisions come from risk estimation coupled with a loss/cost/utility function.
One of the best aspects of a risk (probability) estimation approach is that it handles gray zones where it would be a mistake to make a classification or decision without acquiring more data. And then there is the fact that probability estimation does not require (even does not allow) one to "balance" the outcomes by artificially manipulating the sample.