Improved Nonparallel Support Vector Machine for Pattern Classification

In this paper, we propose a new nonparallel support vector machine for binary classification problems and name it the improved nonparallel support vector machine (IMNSVM). The IMNSVM uses a one-sided ε-band and minimizes ε to achieve a better fitting effect for the same class of training points. By introducing a new variable, ρ, the IMNSVM keeps one class of training points at a certain distance from the hyperplane corresponding to another class of training points, keeping them as far away as possible so as to better adapt to the training points and better describe the difference in data distribution between different categories. The IMNSVM can degenerate into the standard support vector machine (SVM) under certain conditions and is applicable to a wider range of data types. Finally, numerical experiments also explain the effectiveness of the method.