Area of research
Computational Theory and Mathematics · Information Systems
Research interest
Research interests include Computer science, Artificial intelligence, Granularity, Data mining, Pattern recognition (psychology), and Feature selection.
Distributed multi-label feature selection via feature-label information granulation
A Survey on Rough Feature Selection: Recent Advances and Challenges
Multigranularity Information Fused Contrastive Learning With Multiview Clustering
Multimodal multi-instance evidence fusion neural networks for cancer survival prediction
Global-local graph convolutional broad network for hyperspectral image classification
F2CAU-Net: A dual fuzzy medical image segmentation cascade method based on fuzzy feature learning
The fuzzy hypergraph neural network model based on sparse k-nearest neighborhood granule
Class-specific semi-supervised feature selection with fuzzy convex balling information granularity
Margin-Aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification
FCAformer: Fuzzy-Enhanced Class-Aware Attention Based Transformer for Weakly Supervised Histopathology Image Segmentation
A graph regularized overlapping community discovery framework with three-way decisions
FDGC: Fuzzy deep clustering with dual-granularity contrastive learning
Hyperspectral image classification using feature fusion fuzzy graph broad network
AGBN-Transformer: Anatomy-guided brain network transformer for schizophrenia diagnosis
D3WC: Deep three-way clustering with granular evidence fusion
Cascaded Two-Stage Feature Clustering and Selection via Separability and Consistency in Fuzzy Decision Systems
Dynamic evidence fusion neural networks with uncertainty theory and its application in brain network analysis
BiFuG2-Spark: Bi-Directional Fuzzy Granular-Cabin Parallel Attribute Reduction Accelerator With Granular-Group Collaboration
Dual-Channel Fuzzy Interaction Information Fused Feature Selection With Fuzzy Sparse and Shared Granularities
C2F-Explainer: Explaining Transformers Better Through a Coarse-to-Fine Strategy
A distributed attribute reduction based on neighborhood evidential conflict with Apache Spark
Pheromone-guided parallel rough hypercuboid attribute reduction algorithm
MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification
Multi-association evidential feature selection and its application to identifying schizophrenia
Quality-aware fuzzy min–max neural networks for dynamic brain network analysis and its application to schizophrenia identification
FDBFN: Fuzzy discriminative broad fusion network for hyperspectral image classification
FTransCNN: Fusing Transformer and a CNN based on fuzzy logic for uncertain medical image segmentation
RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism
Three-way evidence theory-based density peak clustering with the principle of justifiable granularity
Feature selection in threes: Neighborhood relevancy, redundancy, and granularity interactivity