# A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set

arXiv: https://arxiv.org/abs/2201.03349 · HTML: https://mlsanity.com/paper/2201.03349/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 68 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Infinite Feature Selection: A Graph-based Feature Filtering Approach](https://mlsanity.com/paper/2006.08184/) (2020)
- [Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach](https://mlsanity.com/paper/1707.07538/) (2017)
- [An Efficient and Accurate Rough Set for Feature Selection, Classification and Knowledge Representation](https://mlsanity.com/paper/2201.00436/) (2021)
- [Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality](https://mlsanity.com/paper/1704.05409/) (2017)

## Built on by (most cited first, 12 of 12)

- [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 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)
- [Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary](https://mlsanity.com/paper/2412.13542/) (2024)
- [GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing](https://mlsanity.com/paper/2506.19224/) (2025)
- [A robust three-way classifier with shadowed granular-balls based on justifiable granularity](https://mlsanity.com/paper/2407.11027/) (2024)
- [A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length](https://mlsanity.com/paper/2605.11406/) (2026)
- [Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion](https://mlsanity.com/paper/2504.08937/) (2025)
- [Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification](https://mlsanity.com/paper/2603.29148/) (2026)
- [Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls](https://mlsanity.com/paper/2607.21381/) (2026)
- [3DGBGS: 3D Granular Ball Gaussian Splatting for Compact Novel View Synthesis](https://mlsanity.com/paper/2607.26578/) (2026)

## Abstract (arXiv)

Pawlak rough set and neighborhood rough set are the two most common rough set theoretical models. Pawlak can use equivalence classes to represent knowledge, but it cannot process continuous data; neighborhood rough sets can process continuous data, but it loses the ability of using equivalence classes to represent knowledge. To this end, this paper presents a granular-ball rough set based on the granular-ball computing. The granular-ball rough set can simultaneously represent Pawlak rough sets, and the neighborhood rough set, so as to realize the unified representation of the two. This makes the granular-ball rough set not only can deal with continuous data, but also can use equivalence classes for knowledge representation. In addition, we propose an implementation algorithms of granular-ball rough sets. The experimental results on benchmark datasets demonstrate that, due to the combination of the robustness and adaptability of the granular-ball computing, the learning accuracy of the granular-ball rough set has been greatly improved compared with the Pawlak rough set and the traditional neighborhood rough set. The granular-ball rough set also outperforms nine popular or the state-of-the-art feature selection methods.