Intuitionistic Fuzzy Least Square Projection Twin Support Vector Machine for Pattern Classification

Abstract

The Least Square Projection Twin Support Vector Machine (LSPTSVM) is an effective machine learning tool for solving classification problems. However, LSPTSVM does not account for the contribution of each sample during training, making it susceptible to outliers and noise. This susceptibility diminishes its generalization capability. To remedy this shortcoming, this paper introduces an Intuitionistic Fuzzy LSPTSVM (IFLSPTSVM). This model combines the LSPTSVM with the concept of Intuitionistic Fuzzy Numbers (IFN). In the training process of IFLSPTSVM, the importance of each training sample is gauged using an IFN-based score function that considers its geometric position and surrounding environment. Moreover, the weighted class mean, as opposed to the standard mean used in LSPTSVM, is employed in the calculation of intra-class scatter based on the intuitionistic fuzzy score of the sample. This approach effectively mitigates the impact of noise and outliers and more accurately captures the global information of the class samples. Experimental results on several real-world UCI benchmark datasets and the Case Western Reserve University rolling bearing datasets exhibit the efficacy of the proposed method.