  Competitive methods for multi-label classification typically invest in
learning labels together. To do so in a beneficial way, analysis of label
dependence is often seen as a fundamental step, separate and prior to
constructing a classifier. Some methods invest up to hundreds of times more
computational effort in building dependency models, than training the final
classifier itself. We extend some recent discussion in the literature and
provide a deeper analysis, namely, developing the view that label dependence is
often introduced by an inadequate base classifier, rather than being inherent
to the data or underlying concept; showing how even an exhaustive analysis of
label dependence may not lead to an optimal classification structure. Viewing
labels as additional features (a transformation of the input), we create
neural-network inspired novel methods that remove the emphasis of a prior
dependency structure. Our methods have an important advantage particular to
multi-label data: they leverage labels to create effective units in middle
layers, rather than learning these units from scratch in an unsupervised
fashion with gradient-based methods. Results are promising. The methods we
propose perform competitively, and also have very important qualities of
scalability.
