An Efficient and Adaptive Granular-Ball Generation Method in Classification Problem

Xia, Shuyin; Dai, Xiaochuan; Wang, Guoyin; Gao, Xinbo; Giem, Elisabeth

doi:10.1109/tnnls.2022.3203381

Cited by 33 publications

(9 citation statements)

References 36 publications

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“…Wang [30] first introduced the large-scale cognitive rule into granularcomputing and proposed multi-granular cognitive computing. Xia and Wang [31]- [33] further used hyperspheres of different sizes to represent "grains", and proposed granular-ball computing, in which a large granular-ball represents coarse granu-larity, while a small granular-ball represents fine granularity.…”

Section: Granular Computingmentioning

confidence: 99%

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Xie¹,

Qiao²,

Xia³

et al. 2023

Preprint

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In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granularballs, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.

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Section: Granular Computingmentioning

confidence: 99%

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Xie¹,

Qiao²,

Xia³

et al. 2023

Preprint

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“…The human brain's global precedence cognition is efficient and robust, and is very beneficial for improving the performance of the existing artificial intelligence algorithms. Wang [Wang, 2017] first introduced the large-scale cognitive rule into granular computing and proposed multi-granular cognitive computing. Xia and Wang Xia et al, 2022c;Xia et al, 2022a] further used hyperspheres of different sizes to represent "grains", and proposed granular-ball computing, in which a large granular-ball represents coarse granularity, while a small granular-ball represents fine granularity.…”

Section: Related Work About Granular-ball Computingmentioning

confidence: 99%

“…GBSVM is the first multi-granularity classifier model that is not based on point-input (i.e., containing x i ), and exhibits better efficiency and robustness than the traditional classifier at the same time. Granulular-ball computing is also applied into many other learning methods to improve their generalizability or efficiency, such as rough sets , sampling for classification [Xia et al, 2021], fuzzy sets [Xia et al, 2022b], and so on. In this paper, we want to propose a multi-granularity optimization algorithm based on the idea of granular computing, and make it have the multi-granularity global optimization ability and high performance in efficiency.…”

Section: Related Work About Granular-ball Computingmentioning

confidence: 99%

“…In the field of scientific research and engineering, we often need to calculate the global optimal solution of functions, including unimodal and multi-modal functions. [Hussien et al, 2020;Rashedi et al, 2018;Huband et al, 2006;Weile and Michielssen, 1997]. Multi-modal functions are more difficult, it is easy to fall into local optimal solution and the calculation cost is high.…”

Section: Introductionmentioning

confidence: 99%

“…Our world is displayed in a multi-granularity way. From the perspective of granular computing or granular computing Wang, 2017], the existing intelligent optimization algorithms are optimized based on the most finegrained points, so they do not have the efficiency and global characterization ability of multi-granularity. To this end, we propose a novel optimization algorithm inspired by granularball computing.…”

Section: Introductionmentioning

confidence: 99%

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Granular-ball Optimization Algorithm

Xia¹,

Chen²,

Hou³

et al. 2023

Preprint

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The existing intelligent optimization algorithms are designed based on the finest granularity, i.e., a point. This leads to weak global search ability and inefficiency. To address this problem, we proposed a novel multi-granularity optimization algorithm, namely granular-ball optimization algorithm (GBO), by introducing granular-ball computing. GBO uses many granular-balls to cover the solution space. Quite a lot of small and finegrained granular-balls are used to depict the important parts, and a little number of large and coarse-grained granular-balls are used to depict the inessential parts. Fine multi-granularity data description ability results in a higher global search capability and faster convergence speed. In comparison with the most popular and state-of-the-art algorithms, the experiments on twenty benchmark functions demonstrate its better performance. The faster speed, higher approximation ability of optimal solution, no hyper-parameters, and simpler design of GBO make it an all-around replacement of most of the existing popular intelligent optimization algorithms.

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Algebraic, Topological, and Mereological Foundations of Existential Granules

Mani

2023

Lecture Notes in Computer Science

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An Efficient and Adaptive Granular-Ball Generation Method in Classification Problem

Cited by 33 publications

References 36 publications

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

Granular-ball Optimization Algorithm

Algebraic, Topological, and Mereological Foundations of Existential Granules

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