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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.LG2024
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
Ao Zhou, Bin Liu, Jin Wang +1
The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and mult…
cs.LG2024
AEMLO: AutoEncoder-Guided Multi-Label Oversampling
Ao Zhou, Bin Liu, Jin Wang +2
Class imbalance significantly impacts the performance of multi-label classifiers. Oversampling is one of the most popular approaches, as it augments instances associated with less…