LAUSR

This paper proposes a fuzzy-based Lagrangian twin parametric-margin support vector machine (FLTPMSVM) to reduce the effect of the outliers presented in biomedical data. The proposed FLTPMSVM assigns the weights to each data sample on the basis of fuzzy membership values to reduce the effect of outliers. This paper also adopts the square of the 2-norm of slack variables to make the objective function more convex. The proposed FLTPMSVM solves simple linearly convergent iterative schemes instead of solving a pair of quadratic programming problems. No external toolbox is required for the proposed FLTPMSVM as compared to the other methods. To establish the applicability of the proposed FLTPMSVM in the area of biomedical data classification, numerical experiments are performed on several biomedical datasets. The proposed FLTPMSVM gives an improved generalization performance and reduced training cost as compared to support vector machine (SVM), twin support vector machine (TWSVM), fuzzy twin support vector machine (FTSVM), twin parametric-margin support vector machine (TPMSVM) and new fuzzy twin support vector machine (NFTSVM).