Nu -Improved nonparallel support vector machine

Abstract

In this paper, a nu -improved nonparallel support vector machine (nu -IMNPSVM) is proposed to solve binary classification problems. In this model, we use related ideas of nu -support vector machine(nu -SVM), the parameter nu is introduced to control the limits of the support vectors percentage. In the objective function, the parameter varepsilon is increased to ensure that varepsilon -band is kept as small as possible. It has played a great role in the classification of unbalanced data sets. On the basis of maximizing the interval between two classes, nu -IMNPSVM can fully fit the distribution of data points in the class by minimizing the varepsilon -band, which enhances the generalization ability of the model. The results on the benchmark datasets testify that the proposed model has a good effect on the classification accuracy.