Fuzzy Locality Preserving Projection Twin Support Vector Machine for Classification

Recursive projection twin support vector machine (PTSVM) and Locality preserving projection twin support vector machine (LPPTSVM) are two extensions of traditional support vector machine (SVM). However, they may lead to a weak classifier in the application where some data points may not be fully assigned to one class. In this paper, we introduce the basic idea of fuzzy membership into LPPTSVM and propose a fuzzy LPPTSVM (FLPPTSVM) algorithm. Through fuzzy weighting technique, input points with different confidence degrees can make different contributions to the learning of decision surface. In this way, FLPPTSVM not only maintains the advantages of LPPTSVM which considers the local geometrical structure of the data and enhances the generalization ability of the algorithm, but also decreases the influence of outliers and noises. We construct experiments on artificial and UCI benchmark datasets and the results show the effectiveness and robustness of the proposed FLPPTSVM method.