While it is known that multiple classifier systems can be effective also in multi-label problems, only the classifier fusion approach has been considered so far. In this paper we focus on the classifier selection approach instead. We discuss a specific selection strategy for ensembles of multi-label classifiers, based on selecting one or more two-class classifiers for each class, possibly coming from different multi-label classifiers. We then derive static selection criteria based on the F measure, which is widely used in multi-label problems. Preliminary experimental results show that the considered selection strategy can effectively exploit the complementarity between the multi-label classifiers on the different classes. They also show that the derived selection criteria can improve the trade-off between the macro- and micro-averaged F measure, despite it is known that an increase in either of them is usually attained at the expense of the other one.