1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
Ao Zhou, Bin Liu, Jin Wang +1
Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-bat…