ID  - jn:NIPS-17-MLC-RNN
T1  - Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification
A1  - Nam, Jinseok
A1  - Loza Mencía, Eneldo
A1  - Kim, Hyunwoo J.
A1  - Fürnkranz, Johannes
ED  - Guyon, Isabelle
ED  - von Luxburg, Ulrike
ED  - Bengio, Samy
ED  - Wallach, Hanna M.
ED  - Fergus, Rob
ED  - Vishwanathan, S. V. N.
ED  - Garnett, Roman
TI  - Advances in Neural Information Processing Systems 30 (NIPS-17)
Y1  - 2017
SP  - 5419
EP  - 5429
AD  - Long Beach, CA
UR  - http://papers.nips.cc/paper/7125-maximizing-subset-accuracy-with-recurrent-neural-networks-in-multi-label-classification
N2  - Multi-label classification is the task of predicting a set of labels for a given input instance. 
Classifier chains are a state-of-the-art method for tackling such problems, which essentially converts this problem into a sequential prediction problem, where the labels are first ordered in an arbitrary fashion, and the task is to predict a sequence of binary values for these labels.
In this paper, we replace classifier chains with recurrent neural networks, a sequence-to-sequence prediction algorithm which has recently been successfully applied to sequential prediction tasks in many domains.
The key advantage of this approach is that it allows to focus on the prediction of the positive labels only, a much smaller set than the full set of possible labels.
Moreover, parameter sharing across all classifiers allows to better exploit information of previous decisions.
As both, classifier chains and recurrent neural networks depend on a fixed ordering of the labels, which is typically not part of a multi-label problem specification, we also compare different ways of ordering the label set, and give some recommendations on suitable ordering strategies.
ER  -