Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Abstract

In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification

“…In recently, in stead of using the L1 or L2 norm, Xie et al [24] improve the Lap-LSTSVM to Laplacian Lp norm least squares TSVM (Lap-LpLSTSVM). From the perspective of noise sensitivity and unstable for resampling in the Lap-TSVM , many techniques have been proposed to tackle these problems, such as IFLap-TSVM [25], SIFTSVM [26]. However, there is no theoretical analyst to guarantee the performance of those problems in the current works.…”