5 papers · 1 filter
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…
D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Bin Liu, Haoyu Peng, Zhijia Wei +2
Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically re…
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…
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…
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Bin Liu, Siqi Wu, Jin Wang +2
The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to…