Hypergraph method improves multi-label classification accuracy significantly
HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling
Machine Learning
Summary
Multi-label classification means predicting all the correct labels for something, like tagging pictures with multiple objects. The challenge is labels often appear together in complex ways, which traditional methods don't fully capture. The authors propose HyperLabel, a method that uses a special kind of network called a hypergraph to directly model these multi-way label connections. Their tests show this approach does better than previous methods on several datasets, especially in terms of balanced accuracy across labels.
What this means in practice
- •For machine learning engineers: Improve multi-label classification models by incorporating explicit high-order label dependency structures for better prediction accuracy.
- •For information retrieval teams: Enhance tagging and categorization systems by using hypergraph-based label correlation modeling to capture complex label co-occurrences.
Authors
Peiyu Zhang, Heng Ping, Nikos Kanakaris, Yucheng Zhao, Shixuan Li, Wei Yang, Xiongye Xiao, Paul Bogdan
Abstract
Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dependencies arising from co-occurrence patterns. Existing approaches are limited in capturing high-order label correlations, relying on implicit learning through contrastive objectives or pairwise attention mechanisms without structural guidance. We propose HyperLabel, an encoder-decoder framework that explicitly models label dependencies through hypergraph neural networks. Our contributions are twofold: (i) We construct a label hypergraph where sample-defined hyperedges naturally encode multi-way co-occurrence patterns, providing explicit structural prior knowledge that captures relationships beyond pairwise interactions. (ii) We propose a unified cross-modal learning approach where HGNN+ performs bidirectional message passing to integrate feature information with label structure, and a shared cross-attention decoder processes both modalities through complementary learning objectives. Extensive experiments on seven benchmark datasets demonstrate that HyperLabel achieves state-of-the-art performance, with particularly significant improvements on macro-F1 scores (+10.3% on Delicious, +8.2% on Bibtex), validating that explicit hypergraph structure effectively captures complex label relationships. The code is available at https://github.com/iZHpy/Multi-label_hypergraph .