Most existing fuzzy computing methods use points as input, which is the finest granularity from the perspective of granular computing. Consequently, these classifiers are neither efficient nor robust to label noise. Therefore, we propose a framework for a fuzzy granular-ball computational classifier by introducing granular-ball computing into fuzzy set. The computational framework is based on the granular-balls input rather than points; therefore, it is more efficient and robust than traditional fuzzy methods. Furthermore, the framework is extended to the fuzzy support vector machine (FSVM), and granular ball fuzzy SVM (GBFSVM) is derived. The experimental results demonstrate the effectiveness and efficiency of GBFSVM.

翻译：大多数现有的模糊计算方法都使用点作为输入,这是从颗粒计算角度看最好的颗粒值。 因此,这些分类器对标注噪音既无效率,也不够强力。 因此,我们提出一个框架,通过将颗粒球计算引入模糊的集束,为模糊的颗粒球计算分类器提供一个框架。 计算框架的基础是颗粒球输入而不是点; 因此,它比传统的模糊方法更有效、更健全。 此外,这个框架还扩展到了模糊支持矢量机(FSVM)和颗粒球烟雾SVM(GBFSVM), 实验结果显示了GBFSVM的效益和效率。