# Granular Ball K-Class Twin Support Vector Classifier

arXiv: https://arxiv.org/abs/2412.05438 · HTML: https://mlsanity.com/paper/2412.05438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 6 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [GBSVM: Granular-ball Support Vector Machine](https://mlsanity.com/paper/2210.03120/) (2022)
- [Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.04774/) (2024)
- [GL-TSVM: A robust and smooth twin support vector machine with guardian loss function](https://mlsanity.com/paper/2408.16336/) (2024)
- [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-Balls based Fuzzy Twin Support Vector Machine for Classification](https://mlsanity.com/paper/2408.00699/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Unified Framework for EEG Seizure Detection Using Universum-Integrated Generalized Eigenvalues Proximal Support Vector Machine](https://mlsanity.com/paper/2512.21170/) (2025)

## Abstract (arXiv)

This paper introduces the Granular Ball K-Class Twin Support Vector Classifier (GB-TWKSVC), a novel multi-class classification framework that combines Twin Support Vector Machines (TWSVM) with granular ball computing. The proposed method addresses key challenges in multi-class classification by utilizing granular ball representation for improved noise robustness and TWSVM's non-parallel hyperplane architecture solves two smaller quadratic programming problems, enhancing efficiency. Our approach introduces a novel formulation that effectively handles multi-class scenarios, advancing traditional binary classification methods. Experimental evaluation on diverse benchmark datasets shows that GB-TWKSVC significantly outperforms current state-of-the-art classifiers in both accuracy and computational performance. The method's effectiveness is validated through comprehensive statistical tests and complexity analysis. Our work advances classification algorithms by providing a mathematically sound framework that addresses the scalability and robustness needs of modern machine learning applications. The results demonstrate GB-TWKSVC's broad applicability across domains including pattern recognition, fault diagnosis, and large-scale data analytics, establishing it as a valuable addition to the classification algorithm landscape.