Hybrid multi-label classification model for medical applications based on adaptive synthetic data and ensemble learning
In recent years, both machine learning and computer vision have seen growth in the use of
multi-label categorization. SMOTE is now being utilized in existing research for data …
multi-label categorization. SMOTE is now being utilized in existing research for data …
Perturbation-augmented graph convolutional networks: A graph contrastive learning architecture for effective node classification tasks
Q Guo, X Yang, F Zhang, T Xu - Engineering Applications of Artificial …, 2024 - Elsevier
In the context of recent advances in Graph Convolutional Networks (GCNs) for semi-
supervised learning, a significant highlight is the potential of Graph Contrastive Learning …
supervised learning, a significant highlight is the potential of Graph Contrastive Learning …
Enhancing Graph Convolutional Networks with Progressive Granular Ball Sampling Fusion: A Novel Approach to Efficient and Accurate GCN Training
H Cong, Q Sun, X Yang, K Liu, Y Qian - Information Sciences, 2024 - Elsevier
Graph convolutional network (GCN) has gained considerable attention and has been widely
utilized in graph data analytics. However, training large GCNs presents considerable …
utilized in graph data analytics. However, training large GCNs presents considerable …
Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy
D Qian, K Liu, S Zhang, X Yang - Applied Intelligence, 2024 - Springer
In the realm of machine learning, feature selection emerges as a prevalent data
preprocessing technique, playing a crucial role in enhancing model performance across …
preprocessing technique, playing a crucial role in enhancing model performance across …
Label distribution feature selection based on label-specific features
W Shu, Q Xia, W Qian - Applied Intelligence, 2024 - Springer
Label distribution learning, where deal with label ambiguity by describing the degree of
relevance of each label to a specific instance. As a novel machine learning paradigm, the …
relevance of each label to a specific instance. As a novel machine learning paradigm, the …
Effective attribute reduction algorithm based on fuzzy uncertainties using shared neighborhood granulation
S Gao - IEEE Access, 2024 - ieeexplore.ieee.org
As a very prominent research application of the theory of rough sets, attribute reduction
technique has made significant strides in a lot of fields, including decision making, granular …
technique has made significant strides in a lot of fields, including decision making, granular …