5 citations · 16 across the 12 of their papers we have counts for
12 papers
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…
Granular Ball Guided Masking: Structure-aware Data Augmentation
Shuyin Xia, Fan Chen, Dawei Dai +4
Deep learning models have achieved remarkable success in computer vision but still rely heavily on large-scale labeled data and tend to overfit when data is limited or distribution…
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…
GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing
Shuyin Xia, Xiaoyu Lian, Binbin Sang +2
Fuzzy rough set theory is effective for processing datasets with complex attributes, supported by a solid mathematical foundation and closely linked to kernel methods in machine le…
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…
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…
GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
Jiang Xie, Shuyin Xia, Guoyin Wang +1
Most of the 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 us…
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…
Granular-Ball Fuzzy Set and Its Implementation in SVM
Shuyin Xia, Xiaoyu Lian, Guoyin Wang +2
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing. Consequently, these methods are neither effic…
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…
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…
A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set
Shuyin Xia, Cheng Wang, Guoyin Wang +5
Pawlak rough set and neighborhood rough set are the two most common rough set theoretical models. Pawlak can use equivalence classes to represent knowledge, but it cannot process c…