5 citations · 23 across the 23 of their papers we have counts for
17 papers · 1 filter
Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning
Suzhen Yuan, Qilin Xie, Lifeng Shen +3
Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this sample-level kernel construct…
Adaptive Nearest Neighbors Classifier via Granular Ball Computing
Xiaoyu Lian, Shuyin Xia, Hongxuan He +3
The -Nearest Neighbor~(KNN) algorithm is widely used across various tasks. The selection of the value is a key issue because it significantly impacts performance. In this pa…
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
Guan Wang, Shuyin Xia, Lei Qian +4
Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still face…
GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data
Yewang Chen, Junfeng Li, Shuyin Xia +6
To effectively handle clustering task for large-scale datasets, we propose a novel scalable skeleton clustering algorithm, namely GBSK, which leverages the granular-ball technique…
Granular-Ball-Induced Multiple Kernel K-Means
Shuyin Xia, Yifan Wang, Lifeng Shen +1
Most existing multi-kernel clustering algorithms, such as multi-kernel K-means, often struggle with computational efficiency and robustness when faced with complex data distributio…
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
Guannan Lai, Yihui Feng, Xin Yang +5
Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address chal…
Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
Shuyin Xia, Xinjun Ma, Zhiyuan Liu +3
Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph c…
GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex Data
Shuyin Xia, Bolun Shi, Yifan Wang +3
Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementi…
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
Xuemei Cao, Xin Yang, Shuyin Xia +2
This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown clas…
Multi-granularity Causal Structure Learning
Jiaxuan Liang, Jun Wang, Guoxian Yu +2
Unveil, model, and comprehend the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge eme…
GBG++: A Fast and Stable Granular Ball Generation Method for Classification
Qin Xie, Qinghua Zhang, Shuyin Xia +4
Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular b…
Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method
Shuyin Xia, Guoyin Wang, Xinbo Gao +2
To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence methods, Guoyin Wang and Shuyin Xia proposed…
Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls
Jiang Xie, Qiao Deng, Shuyin Xia +3
In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable probl…
GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems
Shuyin Xia, Xinyu Lin, Guan Wang +4
Optimization problems aim to find the optimal solution, which is becoming increasingly complex and difficult to solve. Traditional evolutionary optimization methods always overlook…
GBSVM: Granular-ball Support Vector Machine
Shuyin Xia, Xiaoyu Lian, Guoyin Wang +3
GBSVM (Granular-ball Support Vector Machine) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular-ball as input, rather than a sing…
GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball
Shuyin Xia, Jiang Xie, Guoyin Wang
Existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually ineffi…
An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation
Shuyin Xia, Xinyu Bai, Guoyin Wang +4
This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has…