A novel relative density and non-membership-based intuitionistic fuzzy twin SVM for class imbalance learning

A major challenge in machine learning is the accurate categorization of data in the presence of noise, outliers, and imbalanced class distributions. Fuzzy support vector machines and their variants have demonstrated potential in handling noise and outliers but struggle to address the challenge of imbalanced datasets. To deal with this problem, in this article, we propose a novel relative density and non-membership-based intuitionistic fuzzy twin support vector machine for class imbalance learning. The method utilizes a $k$-nearest neighbor-based probability density estimation to assess the significance of each training pattern. Furthermore, a novel relative density and non-membership (RDNM) based membership function is employed to effectively distinguish noise and outliers from support vectors. To address the class imbalance problem, majority class training patterns are assigned a score function incorporating the imbalance ratio, while minority class patterns are given a higher membership degree of unity. To validate the superiority of the proposed method, comprehensive experiments, and statistical analyses are conducted over 30 imbalanced benchmark datasets, employing linear and non-linear kernels, and compare the outcomes with the existing approaches. Additionally, the proposed model is applied to the HAM10000 dataset, which consists of dermatoscopic images for skin lesion classification, showcasing its effectiveness in medical applications. The experimental results demonstrate that the proposed model surpasses the baseline models, underscoring its capability for addressing classification challenges in real-world applications with imbalanced class distributions.