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arXiv 2210.03120cs.LGcs.AI
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GBSVM:粒球支持向量机

GBSVM: Granular-ball Support Vector Machine

Chongqing University of Posts and Telecommunications(重庆邮电大学)

Shuyin Xia, Xiaoyu Lian, Guoyin Wang, Xinbo Gao, Jiancu Chen, Xiaoli Peng

AI总结:

本文修复了GBSVM原始模型的错误并推导其对偶模型,设计了粒子群优化与序列最小优化算法进行求解,实验证明GBSVM具有良好的鲁棒性和效率。

摘要:

GBSVM(Granular-ball Support Vector Machine,粒球支持向量机)是一项重要尝试,它使用粒球的由粗到细的粒度作为输入,而不是单一数据点来构建分类器。它是首个输入不包含任何点的分类器。然而,现有模型存在一些错误,且其对偶模型尚未被推导出来。因此,当前算法无法被实现或应用。为了解决这些问题,本文修复了现有GBSVM原始模型的错误,并推导了其对偶模型。此外,设计了粒子群优化算法来求解对偶模型。同时,还精心设计了序列最小优化算法来求解对偶模型。该求解方法比基于粒子群优化的版本更快且更稳定。在UCI基准数据集上的实验结果表明,GBSVM具有良好的鲁棒性和效率。所有代码已在开源库中发布。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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