Browse State-of-the-Art › Hierarchical Multi-label Classification
Hierarchical Multi-label Classification
19 papers with code · 20 benchmarks · 15 datasets archive 2025-07-28
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.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
20 leaderboard tables shown for this task, 20 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 20 until expanded.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
15 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
19 shown of 19 papers with code (48 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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5 Feb 2025 1 repository listedIn this work, we present a novel approach to multi-label chest X-ray (CXR) image classification that enhances clinical interpretability while maintaining a streamlined, single-model, single-run training pipeline.
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10 Sep 2024 1 repository listedIn this work, we apply state-of-the-art self-supervised learning techniques on a large dataset of seafloor imagery, \textit{BenthicNet}, and study their performance for a complex hierarchical multi-label (HML)…
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13 Aug 2024 1 repository listedHierarchical text classification (HTC) is a challenging problem with two key issues: utilizing structural information and mitigating label imbalance.
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21 Jul 2024 1 repository listedRecent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during…
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IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes6 Apr 2024 1 repository listedMemes are one of the most popular types of content used in an online disinformation campaign.
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3 Apr 2024 1 repository listedMemes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion.
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HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification26 Mar 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverifiedExisting self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed…
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24 May 2023 1 repository listed Syntology ran 13 of 17 samples · 4 unverified · 17 pointer-only (licence)Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.
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21 Nov 2022 1 repository listedSocial media has become an important information source for crisis management and provides quick access to ongoing developments and critical information.
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5 Nov 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)For example, a paper can be assigned to several topics in a hierarchy tree.
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1 Jun 2022 1 repository listedWe design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints.
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25 Mar 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Gene annotation addresses the problem of predicting unknown associations between gene and functions (e.
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29 Sep 2021 1 repository listedWe provide theoretical grounding for our method and show experimentally the model's ability to learn the true latent taxonomic structure from data.
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24 Mar 2021 1 repository listedMulti-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes.
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13 Jan 2021 1 repository listedSuch a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label…
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20 Oct 2020 1 repository listedHierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes.
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27 Jul 2020 1 repository listedAlso, learning of PCTs can not exploit the sparsity of data to improve the computational efficiency, which is common in both input (molecular fingerprints, bag of words representations) and output spaces (in multi-label…
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1 Jul 2019 1 repository listedCapsule networks have been shown to demonstrate good performance on structured data in the area of visual inference.
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26 May 2019 1 repository listedThe main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures.
Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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