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Hierarchical Multi-label Classification
3260 papers • 126 benchmarks • 313 datasets
Multi-label classification is a standard machine learning problem in which an object can be associated with multiple labels. A hierarchical multi-label classification (HMC) problem is defined as a multi-label classification problem in which classes are hierarchically organized as a tree or as a directed acyclic graph (DAG), and in which every prediction must be coherent, i.e., respect the hierarchy constraint. The hierarchy constraint states that a datapoint belonging to a given class must also belong to all its ancestors in the hierarchy.
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Most implemented papers
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
L. Jing, Boli Chen, Lin Xiao, Zixin Cai•Sat May 25 2019
A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC and has demonstrated that the new model can realistically capture the complex data structures and further improve the performance forHMLC comparing with the state-of-the-art methods.
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