I'm currently facing a problem in which some of my training examples belong to more than one class at the same time, say, sample $y_i$ pertains to class $A$ and $B$. I was thinking that a solution to it would be to consider that sample as two-fold, i.e., consider it as two samples, one for class $A$ and one for class $B$. However, my problem is that I'm performing a one-vs-all strategy, in which I think this solution may cause numerical errors (the feature matrix would have identical rows)!
Do you know of any reference to this kind of problem (or the technical name for it)?
Thanks in advance!