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Johannes Fürnkranz

2 accepted papers

2019

Learning Context-dependent Label Permutations for Multi-label Classification

ICML 2019oral

A key problem in multi-label classification is to utilize dependencies among the labels. Chaining classifiers are a simple technique for addressing this problem but current algorithms all assume a fixed, static label ordering. In this work, we propose a multi-label classification approach which allo…

Cited by 22SourcePDFScholar
2017

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

NeurIPS 2017spotlight

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 arbitrar…

Cited by 232SourcePDFScholar