GBSVM: Granular-ball Support Vector Machine
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866913229859454976 |
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| author | Xia, Shuyin Lian, Xiaoyu Wang, Guoyin Gao, Xinbo Chen, Jiancu Peng, Xiaoli |
| author_facet | Xia, Shuyin Lian, Xiaoyu Wang, Guoyin Gao, Xinbo Chen, Jiancu Peng, Xiaoli |
| contents | GBSVM (Granular-ball Support Vector Machine) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular-ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, this paper has fixed the errors of the original model of the existing GBSVM, and derived its dual model. Furthermore, a particle swarm optimization algorithm is designed to solve the dual model. The sequential minimal optimization algorithm is also carefully designed to solve the dual model. The solution is faster and more stable than the particle swarm optimization based version. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency. All codes have been released in the open source library at http://www.cquptshuyinxia.com/GBSVM.html or https://github.com/syxiaa/GBSVM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_03120 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | GBSVM: Granular-ball Support Vector Machine Xia, Shuyin Lian, Xiaoyu Wang, Guoyin Gao, Xinbo Chen, Jiancu Peng, Xiaoli Machine Learning Artificial Intelligence GBSVM (Granular-ball Support Vector Machine) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular-ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, this paper has fixed the errors of the original model of the existing GBSVM, and derived its dual model. Furthermore, a particle swarm optimization algorithm is designed to solve the dual model. The sequential minimal optimization algorithm is also carefully designed to solve the dual model. The solution is faster and more stable than the particle swarm optimization based version. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency. All codes have been released in the open source library at http://www.cquptshuyinxia.com/GBSVM.html or https://github.com/syxiaa/GBSVM. |
| title | GBSVM: Granular-ball Support Vector Machine |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2210.03120 |