Hierarchical Multi-label Classification
13 papers with code • 16 benchmarks • 9 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.
Benchmarks
These leaderboards are used to track progress in Hierarchical Multi-label Classification
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Datasets
Cellcycle Funcat
SciHTC
Most implemented papers
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
bcol23/HyperIM
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The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures.
Hierarchical Multi-label Classification of Text with Capsule Networks
Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference.
Oblique Predictive Clustering Trees
Also, 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 classification, examples are often labeled with only a fraction of possible labels).
Coherent Hierarchical Multi-Label Classification Networks
EGiunchiglia/C-HMCNN
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NeurIPS 2020
Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes.
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
soumyac1999/hyperbolic-label-emb-for-hmc
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EACL 2021
Such 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 co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.
Multi-Label Classification Neural Networks with Hard Logical Constraints
EGiunchiglia/CCN
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Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes.
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings
iesl/box-mlc-iclr-2022
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ICLR 2022
We provide theoretical grounding for our method and show experimentally the model's ability to learn the true latent taxonomic structure from data.
Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification
Gene annotation addresses the problem of predicting unknown associations between gene and functions (e. g., biological processes) of a specific organism.
Semantic Probabilistic Layers for Neuro-Symbolic Learning
We 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.
Hierarchical Multi-Label Classification of Scientific Documents
msadat3/scihtc
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For example, a paper can be assigned to several topics in a hierarchy tree.
