A novel cost-sensitive three-way intuitionistic fuzzy large margin classifier

Abstract

Three-way decision (3WD) has been widely applied in diverse fields in tackling uncertainty, particularly in classification domain. As a discriminative learning algorithm, Large Margin Distribution Machine (LDM) aims to maximize the inter-class margin by leveraging the marginal distribution of samples, which captures the underlying structure and achieves superior classification performance. However, existing LDMs still suffer from some inherent flaws, including: i) the neglect of fuzzy decision items, leading to the inadequate handling of uncertainty; ii) the utilization of a cost-insensitive mechanism, resulting in high misclassification costs; and iii) the disregard of sample credibility, contributing to the noise susceptibility. To address these limitations, we introduce the intuitionistic fuzzy (IF) and cost-sensitive 3WD (CS3WD), presenting an innovative model known as the CS3W-IFLMC model. The IF theory is employed to quantify sample confidence levels, augmenting noise suppression in our proposed model. Moreover, the integration of the CS3WD method effectively reduces the overall decision cost and further improves the handling of uncertainty in datasets. Consequently, the CS3W-IFLMC model demonstrates superior noise resilience and generalization performance. Our comparative experiments validate the efficacy of the proposed CS3W-IFLMC model in achieving robustness to noise while upholding a competitive classification performance.