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cs.LG2026
Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
Bin Liu, Jun Wu, Haoyu Peng +4
The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent label…
cs.LG2026
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…