Granular-Ball Fuzzy Set and Its Implementation in SVM

TL;DR

This paper introduces a novel granular-ball fuzzy set framework that enhances efficiency and robustness over traditional fuzzy methods by using granular-balls instead of points, and extends it to a fuzzy SVM classifier.

Contribution

It proposes a new granular-ball fuzzy set framework and its extension to fuzzy SVM, improving robustness and efficiency in fuzzy data processing.

Findings

GBFSVM demonstrates superior efficiency in experiments.

The framework is more robust to label noise.

Effective in various fuzzy data processing applications.

Abstract

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.