# GBSVM: Granular-ball Support Vector Machine

arXiv: https://arxiv.org/abs/2210.03120 · HTML: https://mlsanity.com/paper/2210.03120/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 64 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set](https://mlsanity.com/paper/2201.03349/) (2022)

## Built on by (most cited first, 10 of 10)

- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing](https://mlsanity.com/paper/2409.16735/) (2024)
- [Graph-based Representation for Image based on Granular-ball](https://mlsanity.com/paper/2303.02388/) (2023)
- [GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex Data](https://mlsanity.com/paper/2410.13917/) (2024)
- [Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing](https://mlsanity.com/paper/2410.17338/) (2024)
- [Granular Ball Twin Support Vector Machine with Universum Data](https://mlsanity.com/paper/2412.03375/) (2024)
- [Granular-Ball Fuzzy Set and Its Implementation in SVM](https://mlsanity.com/paper/2210.11675/) (2022)
- [GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing](https://mlsanity.com/paper/2303.01082/) (2023)
- [Granular Ball K-Class Twin Support Vector Classifier](https://mlsanity.com/paper/2412.05438/) (2024)
- [Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls](https://mlsanity.com/paper/2303.03590/) (2023)

## Abstract (arXiv)

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 single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, this paper has fixed the errors of the original model of the existing GBSVM, and derived its dual model. Furthermore, a particle swarm optimization algorithm is designed to solve the dual model. The sequential minimal optimization algorithm is also carefully designed to solve the dual model. The solution is faster and more stable than the particle swarm optimization based version. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency. All codes have been released in the open source library at http://www.cquptshuyinxia.com/GBSVM.html or https://github.com/syxiaa/GBSVM.