A Modified PSVM and its Application to Unbalanced Data Classification

A modified proximal support vector machine (MPSVM) is presented for the case of unbalanced data classification in many applications. The algorithm assigns different penalty coefficients to the positive and negative samples respectively by adding a new diagonal matrix in the primal optimization problem. And further the decision function is obtained. In addition, the real-coded immune clone algorithm (RICA) is employed to select the global optimal parameters to get the high generalization performance. The experimental results illustrate the effectiveness of the proposed method.