Mohammed Zakaria Moustafa1, Mohammed Rizk Mohammed1, Hatem Awad Khater2 and Hager Ali Yahia1, 1Alexandria University, Egypt, 2HORAS University, Egypt
A support vector machine (SVM) learns the decision surface from two different classes of the input points, in many applications there are misclassifications in some of the input points. In this paper a biobjective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The experimental results, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points.
Support vector machine (SVMs), Classification, Multi-objective problems, weighting method, Quadratic programming.