OK, so [I found a review paper on this topic](http://talos.csd.auth.gr/tsoumakas/publications/J6.pdf) that's widely cited. It describes a couple of methods to transform multi-label problems into single-label ones (including the one I described), as well as training single-label classifiers and combining them, and a couple of of specialised classifiers which deal with multi-labels specifically, so it doesn't look like there's a "right" answer here. One potential option might be a specialised *MultiCategoricalAttribute* type, which instead of just storing a single value, could store a set of label values and be referred to as the "class" for the purposes of storage (this would require additional engineering work because you might end up with with widths greater than 64 bits). I give an example in *KNN* of how I propose outputting predictions using *Instances*, so multi-label regression classifiers could simply add multiple *FloatAttributes* to their response.