Support vector machine constructs an optimal hyperplane utilizing a small set of vectors near boundary. However, when the two-class problem samples are very unbalanced, PSVM tends to fit better the class with more samples and has high error in fewer samples. To solve the problem, an improved SVM algorithm, DFP-PSVM, is presented in this paper. Furthermore, this drawback exists in one-from-the-rest approach to multi-classes. A multi-class classification algorithm using quasiNewton is proposed based on DFP-PSVM. Simulated exampl...
