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Nearest labelset using double distances for multi-label classification

Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels. In this article we propose a novel approach, Nearest Labelset using D...

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Auteurs principaux: Hyukjun Gweon, Matthias Schonlau, Stefan H. Steiner
Format: Artigo
Langue:Inglês
Publié: PeerJ Inc. 2019-12-01
Collection:PeerJ Computer Science
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Accès en ligne:https://peerj.com/articles/cs-242.pdf
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