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Hierarchical Multi-label Classification

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.

Papers

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Showing 31–40 of 48 papers

TitleStatusHype
Acoustic identification of individual animals with hierarchical contrastive learning—0
All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)—0
An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction—0
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks—0
Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?—0
Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate—0
Enhancing Classification with Hierarchical Scalable Query on Fusion Transformer—0
Evaluating Extreme Hierarchical Multi-label Classification—0
Evaluating Extreme Hierarchical Multi-label Classification—0
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification—0
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