# GBTSVR: Twin Support Vector Regression via Granular Ball Computing

arXiv: https://arxiv.org/abs/2610.20053 · HTML: https://mlsanity.com/paper/2610.20053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 9 of 9)

- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [Comprehensive Review On Twin Support Vector Machines](https://mlsanity.com/paper/2105.00336/) (2021)
- [Deep Learning for Brain Age Estimation: A Systematic Review](https://mlsanity.com/paper/2212.03868/) (2022)
- [Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine](https://mlsanity.com/paper/2408.01713/) (2024)
- [Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.04774/) (2024)
- [TRKM: Twin Restricted Kernel Machines for Classification and Regression](https://mlsanity.com/paper/2502.15759/) (2025)
- [Twin support vector quantile regression](https://mlsanity.com/paper/2305.03894/) (2023)
- [Enhanced Feature Based Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.05786/) (2024)
- [Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing](https://mlsanity.com/paper/2410.17338/) (2024)

## Abstract (arXiv)

Regression models used in real-world applications are often challenged by noise, outliers, data heterogeneity, and asymmetric error characteristics, which severely limit the effectiveness of conventional point-wise learning models. While twin support vector regression (TSVR) partially addresses these issues through the use of nonparallel regression bounds, it still relies on individual samples as basic learning units. As a result, TSVR remains highly sensitive to noisy and contaminated observations and fails to leverage the underlying structural and regional information inherent in complex datasets, leading to suboptimal robustness and generalization performance. To address these limitations, we introduce granular ball twin support vector regression (GBTSVR) and its quantile extension, granular ball twin support vector quantile regression (GB-TSVQR). The proposed models integrate granular ball computing with TSVR, replacing individual data points with compact, structure-preserving granular balls as fundamental learning units. This region-level representation effectively suppresses noise and outliers while preserving intrinsic data geometry, thereby enhancing robustness, stability, and generalization. GBTSVR constructs nonparallel lower- and upper-bound regression functions over granular balls, significantly reducing sensitivity to sample-level perturbations. Building upon this framework, GB-TSVQR further incorporates quantile regression principles to simultaneously capture heterogeneous trends and asymmetric error distributions across different quantile levels. Extensive experiments on synthetic datasets, time-series benchmarks, and outlier-contaminated data demonstrate that the proposed GBTSVR and GB-TSVQR consistently outperform state-of-the-art regression models.