Granular-Ball Fuzzy Set and Its Implementation in SVM
This work addresses efficiency and robustness issues in fuzzy data processing for machine learning applications, though it appears incremental as it builds on existing fuzzy set and SVM methods.
The authors tackled the inefficiency and noise sensitivity of traditional fuzzy set methods by proposing a granular-ball fuzzy set framework, which uses granular-balls instead of points as input, and extended it to create GBFSVM, achieving improved performance in experiments.
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.