论文标题
GBSVM：颗粒球支持向量机
GBSVM: Granular-ball Support Vector Machine
论文作者
论文摘要
GBSVM（颗粒球支持向量机）是使用颗粒球的粗到细粒度构建分类器的重要尝试，而不是单个数据点。它是第一个分类器，其输入不包含任何点。但是，现有模型有一些错误，并且其双重模型尚未得出。结果，当前算法无法实现或应用。为了解决这些问题，本文修复了现有GBSVM原始模型的错误，并得出了双重模型。此外，粒子群优化算法旨在解决双重模型。顺序最小优化算法也经过精心设计以求解双重模型。该解决方案比基于粒子群优化的版本更快，更稳定。 UCI基准数据集的实验结果表明，GBSVM具有良好的鲁棒性和效率。所有代码均已在开源库中发布，网址为http://www.cquptshuyinxia.com/gbsvm.html或https://github.com/syxiaa/gbsvm。
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.
