Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification

NeurIPS 2017 Jinseok NamEneldo Loza MencíaHyunwoo J. KimJohannes Fürnkranz

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... (read more)

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