Coherent Hierarchical Multi-Label Classification Networks

• URL: http://arxiv.org/abs/2010.10151v1
• Date: Tue, 20 Oct 2020 09:37:02 GMT
• Title: Coherent Hierarchical Multi-Label Classification Networks
• Authors: Eleonora Giunchiglia, Thomas Lukasiewicz
• Abstract summary: C-HMCNN(h) is a novel approach for HMC problems, which exploits 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.
• Score: 56.41950277906307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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