  Hierarchical multi-label classification (HMC) is a challenging classification
task extending standard multi-label classification problems by imposing a
hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a
novel approach for HMC problems, which, given a network h for the underlying
multi-label classification problem, exploits the hierarchy information in order
to produce predictions coherent with the constraint and improve performance. We
conduct an extensive experimental analysis showing the superior performance of
C-HMCNN(h) when compared to state-of-the-art models.
