Volume 11, Number 2

A BI-objective Model for SVM With an Interactive Procedure to Identify the Best Compromise Solution


Mohammed Zakaria Moustafa1, Mohammed Rizk Mohammed1, Hatem Awad Khater2 and Hager Ali Yahia1, 1ALEXANDRIA University, Egypt, and 2Horus University, Egypt


A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective 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 numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.


Support vector machine (SVMs); Classification; Multi-objective problems; weighting method; Quadratic programming; interactive approach.