# An Efficient and Adaptive Granular-ball Generation Method in Classification Problem

arXiv: https://arxiv.org/abs/2201.04343 · HTML: https://mlsanity.com/paper/2201.04343/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 51 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Built on by (most cited first, 30 of 33)

- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [GBG++: A Fast and Stable Granular Ball Generation Method for Classification](https://mlsanity.com/paper/2305.18450/) (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)
- [Graph-based Representation for Image based on Granular-ball](https://mlsanity.com/paper/2303.02388/) (2023)
- [Open Continual Feature Selection via Granular-Ball Knowledge Transfer](https://mlsanity.com/paper/2403.10253/) (2024)
- [Granular-ball Representation Learning for Deep CNN on Learning with Label Noise](https://mlsanity.com/paper/2409.03254/) (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)
- [MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering](https://mlsanity.com/paper/2605.08759/) (2026)
- [Granular-Ball Fuzzy Set and Its Implementation in SVM](https://mlsanity.com/paper/2210.11675/) (2022)
- [A robust three-way classifier with shadowed granular-balls based on justifiable granularity](https://mlsanity.com/paper/2407.11027/) (2024)
- [Granular-Balls based Fuzzy Twin Support Vector Machine for Classification](https://mlsanity.com/paper/2408.00699/) (2024)
- [GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data](https://mlsanity.com/paper/2509.23742/) (2025)
- [Granular Ball Guided Masking: Structure-aware Data Augmentation](https://mlsanity.com/paper/2512.21011/) (2025)
- [A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length](https://mlsanity.com/paper/2605.11406/) (2026)
- [Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization](https://mlsanity.com/paper/2606.12077/) (2026)
- [Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls](https://mlsanity.com/paper/2303.03590/) (2023)
- [Algebraic, Topological, and Mereological Foundations of Existential Granules](https://mlsanity.com/paper/2308.16157/) (2023)
- [EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning](https://mlsanity.com/paper/2412.10461/) (2024)
- [GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning](https://mlsanity.com/paper/2412.14561/) (2024)
- [GBSVR: Granular Ball Support Vector Regression](https://mlsanity.com/paper/2503.10539/) (2025)
- [LGBQPC: Local Granular-Ball Quality Peaks Clustering](https://mlsanity.com/paper/2505.11359/) (2025)
- [Approximate Borderline Sampling using Granular-Ball for Classification Tasks](https://mlsanity.com/paper/2506.02366/) (2025)
- [GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure](https://mlsanity.com/paper/2506.02390/) (2025)
- [Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting](https://mlsanity.com/paper/2603.24106/) (2026)
- [Multi-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion](https://mlsanity.com/paper/2603.26729/) (2026)
- [Square Superpixel Generation and Representation Learning via Granular Ball Computing](https://mlsanity.com/paper/2603.29460/) (2026)
- [SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing](https://mlsanity.com/paper/2605.02617/) (2026)
- [Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls](https://mlsanity.com/paper/2607.21381/) (2026)

## Abstract (arXiv)

Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace k-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granular-ball's overlap eliminating and some other factors. This makes the granular-ball generation process of parameter-free and completely adaptive in the true sense. In addition, this paper first provides the mathematical models for the granular-ball covering. The experimental results on some real data sets demonstrate that the proposed two granular-ball generation methods have similar accuracies with the existing method while adaptiveness or acceleration is realized.