Optimizing Classification of Infrequent Labels by Reducing Variability in Label Distribution
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Abstract (arXiv)
This paper presents a novel solution, LEVER, designed to address the challenges posed by underperforming infrequent categories in Extreme Classification (XC) tasks. Infrequent categories, often characterized by sparse samples, suffer from high label inconsistency, which undermines classification performance. LEVER mitigates this problem by adopting a robust Siamese-style architecture, leveraging knowledge transfer to reduce label inconsistency and enhance the performance of One-vs-All classifiers. Comprehensive testing across multiple XC datasets reveals substantial improvements in the handling of infrequent categories, setting a new benchmark for the field. Additionally, the paper introduces two newly created multi-intent datasets, offering essential resources for future XC research.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 7 Nov 2025.
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References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2023Simple and Asymmetric Graph Contrastive Learning without Augmentationscited by 38
- 2022Evaluating Self-Supervised Learning for Molecular Graph Embeddingscited by 22
- 2022Mixed Graph Contrastive Network for Semi-Supervised Node Classificationcited by 21
- 2024Improved Graph-based semi-supervised learning Schemescited by 2
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