  Multi-label classification deals with the problem where each instance is
associated with multiple class labels. Because evaluation in multi-label
classification is more complicated than single-label setting, a number of
performance measures have been proposed. It is noticed that an algorithm
usually performs differently on different measures. Therefore, it is important
to understand which algorithms perform well on which measure(s) and why. In
this paper, we propose a unified margin view to revisit eleven performance
measures in multi-label classification. In particular, we define label-wise
margin and instance-wise margin, and prove that through maximizing these
margins, different corresponding performance measures will be optimized. Based
on the defined margins, a max-margin approach called LIMO is designed and
empirical results verify our theoretical findings.
