Kernel-Target Alignment Based Fuzzy Lagrangian Twin Bounded Support Vector Machine

Abstract

To improve the generalization performance, we develop a new technique for handling the impacts of outliers using Lagrangian twin bounded SVM (TBSVM) with kernel fuzzy membership values, which is termed kernel-target alignment-based fuzzy Lagrangian twin bounded support vector machine (KTA-FLTBSVM). Here, the objective functions are having L2-norm vectors of the slack variable that leads to the optimization problem more convex and yields a unique global solution. Also, the fuzzy membership values are employing the importance of data samples assigned to each sample to minimize the impacts of outlier and noise. Further, we have suggested a linearly convergent iterative approach to obtain the solution of the problem unlike in place to solve the quadratic programming problem in Twin SVM (TSVM) and TBSVM. To investigate the effectiveness of the proposed KTA-FLTBSVM, the comprehensive experiments demonstrate with other reported models on artificial datasets along with benchmark real-life publicly available datasets. Our KTA-FLTBSVM outperforms to other models in terms of better classification accuracy.