4 papers
SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing
Genhao Tian, Taihua Xu, Shuyin Xia +3
Capturing semantic consistency among nodes is crucial for effective graph representation learning. Existing approaches typically rely on -nearest neighbors (NN) or other node…
Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion
Chengjie Cui, Taihua Xu, Shuyin Xia +3
The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation…
GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure
Qin Xie, Qinghua Zhang, Shuyin Xia +2
Adaptive Boosting (AdaBoost) faces significant challenges posed by label noise, especially in multiclass classification tasks. Existing methods either lack mechanisms to handle lab…
Approximate Borderline Sampling using Granular-Ball for Classification Tasks
Qin Xie, Qinghua Zhang, Shuyin Xia
Data sampling enhances classifier efficiency and robustness through data compression and quality improvement. Recently, the sampling method based on granular-ball (GB) has shown pr…