Polycentric intuitionistic fuzzy weighted least squares twin SVMs

Abstract

The classification of data with outliers and noise has always been one of the principal challenges within machine learning. The previous unicentric-based fuzzy twin support vector machines (SVMs) typically allot the membership through proximity to the center of the samples, which neglects the global structural information and the local neighborhood information and potentially causes confusion between fringe support vectors and outliers. In this paper, a polycentric intuitionistic fuzzy weighted least squares twin SVMs (PIFW-LSTSVM) is presented to alleviate the above issue. Concretely, the PIFW-LSTSVM model simultaneously assigns membership and nonmembership to each sample, where the membership is determined by the sample proximity to the corresponding nearest center, and nonmembership is identified by neighborhood entropy. Benefiting from the novel polycentric weighting strategy, the PIFW-LSTSVM model mitigates the impact of outliers and noise and reduces the confusion between fringe support vectors and outliers or noise, thereby boosting the generalization ability. The experiments, conducted on both artificial and real-world benchmark datasets, comprehensively demonstrate the effectiveness and superiority of the PIFW-LSTSVM model compared to other state-of-the-art models.