Related papers: GBSVM: Granular-ball Support Vector Machine

GBSVM: Granular-ball Support Vector Machine

URL: http://arxiv.org/abs/2210.03120v2
Date: Sun, 11 Feb 2024 16:02:18 GMT
Title: GBSVM: Granular-ball Support Vector Machine
Authors: Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Xinbo Gao, Jiancu Chen, Xiaoli Peng
Abstract summary: GBSVM 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. This paper has fixed the errors of the original model of the existing GBSVM, and derived its dual model. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency.
Score: 46.60182022640765
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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