# GBG++: A Fast and Stable Granular Ball Generation Method for Classification

arXiv: https://arxiv.org/abs/2305.18450 · HTML: https://mlsanity.com/paper/2305.18450/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 62 references that are in the map.
- First posted on arXiv 29 May 2023.

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

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier](https://mlsanity.com/paper/1905.11543/) (2019)
- [G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space](https://mlsanity.com/paper/2006.11510/) (2020)

## Built on by (most cited first, 13 of 13)

- [Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity](https://mlsanity.com/paper/2405.06904/) (2024)
- [Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary](https://mlsanity.com/paper/2412.13542/) (2024)
- [MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering](https://mlsanity.com/paper/2605.08759/) (2026)
- [A robust three-way classifier with shadowed granular-balls based on justifiable granularity](https://mlsanity.com/paper/2407.11027/) (2024)
- [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)
- [GBRIP: Granular Ball Representation for Imbalanced Partial Label Learning](https://mlsanity.com/paper/2412.14561/) (2024)
- [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)
- [Implicit Non-Causal Factors are Out via Dataset Splitting for Domain Generalization Object Detection](https://mlsanity.com/paper/2601.19127/) (2026)
- [Granular Ball Guided Stable Latent Domain Discovery for Domain-General Crowd Counting](https://mlsanity.com/paper/2603.24106/) (2026)
- [QGB-WkNN: Quantum Granular-Ball Learning for Robust Classification](https://mlsanity.com/paper/2609.05952/) (2026)

## Abstract (arXiv)

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on k-means or k-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB instead of randomly selecting the center and calculating the distances between it and all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based k-nearest neighbors algorithm (GBkNN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on 24 public benchmark datasets. The implementation code of experiments is available at https://github.com/CherylTse/GBG-plusplus.