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Extreme Multi-Label Classification

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.

Papers

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Showing 51–60 of 75 papers

TitleStatusHype
Learning from eXtreme Bandit Feedback—0
Probabilistic Label Trees for Extreme Multi-label ClassificationCode1
On Data Augmentation for Extreme Multi-label Classification—0
Unbiased Loss Functions for Extreme Classification With Missing Labels—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model—0
Extreme Multi-label Classification from Aggregated Labels—0
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification—0
Taming Pretrained Transformers for Extreme Multi-label Text ClassificationCode0
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label ClassificationCode0
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