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Coherent Hierarchical Multi-Label Classification Networks
Eleonora Giunchiglia · Thomas Lukasiewicz

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1127

Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes. In this paper, we propose C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label classification problem, exploits the hierarchy information in order to produce predictions coherent with the constraint and improve performance. We conduct an extensive experimental analysis showing the superior performance of C-HMCNN(h) when compared to state-of-the-art models.

Author Information

Eleonora Giunchiglia (University of Oxford)
Thomas Lukasiewicz (University of Oxford)

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