Contrastive learning-based fuzzy support vector machine

Abstract

SVM faces significant challenges when dealing with noisy data, particularly in the context of complex data distributions and high levels of noise. Existing models, while offering some improvements in robustness, struggle with adapting to various noise types, handling extreme outliers, and overly relying on classification boundaries, which affects stability and classification accuracy. To address these limitations, this paper proposes a novel Fuzzy Support Vector Machine model based on contrastive learning, designed to enhance robustness to noise and improve generalization. The proposed model incorporates neighborhood structural information through contrastive learning, which refines the evaluation of slack variables and reduces the impact of noisy and outlier samples. Additionally, a redesigned fuzzy membership fusion mechanism is introduced, enabling more accurate handling of uncertain data. The experimental results on the benchmark dataset indicate that the average rankings of the accuracy and G-mean of CL-FSVM with linear and Gaussian kernels are below 2, which is superior to several excellent FSVM variants, demonstrating the effectiveness of this model.