  Multiclass decomposition splits a multiclass classification problem into a
series of independent binary learners and recomposes them by combining their
outputs to reconstruct the multiclass classification results. Three widely-used
realizations of such decomposition methods are One-Versus-All (OVA),
One-Versus-One (OVO), and Error-Correcting-Output-Code (ECOC). While OVA and
OVO are quite simple, both of them assume all classes are orthogonal which
neglect the latent correlation between classes in real-world.
Error-Correcting-Output-Code (ECOC) based decomposition methods, on the other
hand, are more preferable due to its integration of the correlation among
classes. However, the performance of existing ECOC-based methods highly depends
on the design of coding matrix and decoding strategy. Unfortunately, it is
quite uncertain and time-consuming to discover an effective coding matrix with
appropriate decoding strategy. To address this problem, we propose LightMC, an
efficient dynamic multiclass decomposition algorithm. Instead of using fixed
coding matrix and decoding strategy, LightMC uses a differentiable decoding
strategy, which enables it to dynamically optimize the coding matrix and
decoding strategy, toward increasing the overall accuracy of multiclass
classification, via back propagation jointly with the training of base learners
in an iterative way. Empirical experimental results on several public
large-scale multiclass classification datasets have demonstrated the
effectiveness of LightMC in terms of both good accuracy and high efficiency.
