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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 21–30 of 48 papers

TitleStatusHype
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification—0
HierLPR: Decision making in hierarchical multi-label classification with local precision rates—0
TLMCM Network for Medical Image Hierarchical Multi-Label Classification—0
A Capsule Network for Hierarchical Multi-Label Image Classification—0
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
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