  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.
