A robust fuzzy least squares twin support vector machine for class imbalance learning

Abstract

Twin support vector machine is one of the most prominent techniques for classification problems. It has been applied in various real world applications due to its less computational complexity. In most of the applications on classification, there is imbalance in the number of samples of the classes which leads to incorrect classification of the data points of the minority class. Further, while dealing with imbalanced data, noise poses a major challenge in various applications. To resolve these problems, in this paper we propose a robust fuzzy least squares twin support vector machine for class imbalance learning termed as RFLSTSVM-CIL using 2-norm of the slack variables which makes the optimization problem strongly convex. In order to reduce the effect of outliers, we propose a novel fuzzy membership function specifically for class imbalance problems. Our proposed function gives the appropriate weights to the datasets and also incorporates the knowledge about the imbalance ratio of the data. In our proposed model, a pair of system of linear equations is solved instead of solving a quadratic programming problem (QPP) which makes our model efficient in terms of computation complexity. To check the performance of our proposed approach, several numerical experiments are performed on synthetic and real world benchmark datasets. Our proposed model RFLSTSVM-CIL has shown better generalization performance in comparison to the existing methods in terms of AUC and training time