# GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing

arXiv: https://arxiv.org/abs/2409.16735 · HTML: https://mlsanity.com/paper/2409.16735/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Random vector functional link network: recent developments, applications, and future directions](https://mlsanity.com/paper/2203.11316/) (2022)
- [Random Vector Functional Link Neural Network based Ensemble Deep Learning](https://mlsanity.com/paper/1907.00350/) (2019)
- [Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning](https://mlsanity.com/paper/2307.07881/) (2023)
- [Deep Learning for Brain Age Estimation: A Systematic Review](https://mlsanity.com/paper/2212.03868/) (2022)
- [Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers](https://mlsanity.com/paper/2307.08713/) (2023)
- [Intuitionistic Fuzzy Generalized Eigenvalue Proximal Support Vector Machine](https://mlsanity.com/paper/2408.01713/) (2024)
- [GBSVM: Granular-ball Support Vector Machine](https://mlsanity.com/paper/2210.03120/) (2022)
- [Wave-RVFL: A Randomized Neural Network Based on Wave Loss Function](https://mlsanity.com/paper/2408.02824/) (2024)

## Built on by (most cited first, 7 of 7)

- [CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks](https://mlsanity.com/paper/2605.12580/) (2026)
- [Enhancing Robustness and Efficiency of Least Square Twin SVM via Granular Computing](https://mlsanity.com/paper/2410.17338/) (2024)
- [RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets](https://mlsanity.com/paper/2510.06278/) (2025)
- [LGBQPC: Local Granular-Ball Quality Peaks Clustering](https://mlsanity.com/paper/2505.11359/) (2025)
- [Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls](https://mlsanity.com/paper/2607.21381/) (2026)
- [RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network](https://mlsanity.com/paper/2608.16965/) (2026)
- [GBFRVFL: Granular-Ball Computing-Based Fuzzy Random Vector Functional Link Network](https://mlsanity.com/paper/2609.29670/) (2026)

## Abstract (arXiv)

The random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the need for inverting the entire training matrix. To address these issues, we propose granular ball RVFL (GB-RVFL) model, which uses granular balls (GBs) as inputs instead of training samples. This approach enhances scalability by requiring only the inverse of the GB center matrix and improves robustness against noise and outliers through the coarse granularity of GBs. Furthermore, RVFL overlooks the dataset's geometric structure. To address this, we propose graph embedding GB-RVFL (GE-GB-RVFL) model, which fuses granular computing and graph embedding (GE) to preserve the topological structure of GBs. The proposed GB-RVFL and GE-GB-RVFL models are evaluated on KEEL, UCI, NDC and biomedical datasets, demonstrating superior performance compared to baseline models.