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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
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification—0
Label Disentanglement in Partition-based Extreme Multilabel Classification—0
DiSMEC - Distributed Sparse Machines for Extreme Multi-label ClassificationCode0
Dense Retrieval as Indirect Supervision for Large-space Decision MakingCode0
Dual-Encoders for Extreme Multi-Label ClassificationCode0
Learning a Compressed Sensing Measurement Matrix via Gradient UnrollingCode0
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood AggregationCode0
Deep Extreme Multi-label LearningCode0
Priberam at MESINESP Multi-label Classification of Medical Texts TaskCode0
A no-regret generalization of hierarchical softmax to extreme multi-label classificationCode0
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