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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 26–48 of 48 papers

TitleStatusHype
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
Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation—0
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit—0
Notes on hierarchical ensemble methods for DAG-structured taxonomies—0
Semantic HMC for Big Data Analysis—0
Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification—0
Academic Resource Text Level Multi-label Classification based on Attention—0
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior KnowledgeCode0
Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task LearningCode0
Solution for the EPO CodeFest on Green Plastics: Hierarchical multi-label classification of patents relating to green plastics using deep learningCode0
Clinically-Inspired Hierarchical Multi-Label Classification of Chest X-rays with a Penalty-Based Loss FunctionCode0
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat ImageryCode0
Hierarchical Multi-label Classification of Text with Capsule NetworksCode0
Hyperbolic Interaction Model For Hierarchical Multi-Label ClassificationCode0
IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in MemesCode0
Oblique Predictive Clustering TreesCode0
Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label ClassificationCode0
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