论文标题
粒状球模糊集及其在SVM中的实现
Granular-Ball Fuzzy Set and Its Implementation in SVM
论文作者
论文摘要
大多数现有的模糊集方法都使用点作为其输入，这是粒状计算的角度最好的粒度。因此，这些方法既不有效，也不是鲁棒来标记噪声。因此，我们通过将粒状球计算引入模糊集，提出了一个称为“粒状 - 球模糊”的框架。计算框架基于粒状球输入而不是点。因此，它比传统的模糊方法更有效，更健壮，可以根据其可扩展性在模糊数据处理的各个领域中使用。此外，该框架扩展到分类器模糊支持向量机（FSVM），以得出粒状球模糊SVM（GBFSVM）。实验结果证明了GBFSVM的有效性和效率。
Most existing fuzzy set methods use points as their input, which is the finest granularity from the perspective of granular computing. Consequently, these methods are neither efficient nor robust to label noise. Therefore, we propose a frame-work called granular-ball fuzzy set 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, and can be used in various fields of fuzzy data processing according to its extensibility. Furthermore, the framework is extended to the classifier fuzzy support vector machine (FSVM), to derive the granular ball fuzzy SVM (GBFSVM). The experimental results demonstrate the effectiveness and efficiency of GBFSVM.
