Iranian Institute of Industrial Engineering Journal of Industrial and Systems Engineering 1735-8272 11 2 2018 08 01 A New Formulation for Cost-Sensitive Two Group Support Vector Machine with Multiple Error Rate 21 30 59552 EN Amir Abbas Najafi Faculty of Industrial Engineering, K.N.Toosi University of Technology Ali Nedaie Faculty of Industrial Engineering, K.N.Toosi University of Technology Journal Article 2016 10 09 Support vector machine (SVM) is a popular classification technique which classifies data using a max-margin separator hyperplane. The normal vector and bias of the mentioned hyperplane is determined by solving a quadratic model implies that SVM training confronts by an optimization problem. Among of the extensions of SVM, cost-sensitive scheme refers to a model with multiple costs which considers different error rates for misclassification. The cost-sensitive scheme is useful when misclassifications cannot be considered equal. For example, it is true for medical diagnosis. In such cases, misclassifying a patient as healthy implies more loss in comparison to the opposite loss. Therefore, cost-sensitive scheme poses as a modified model and hereby aims at minimizing loss function instead of generalization error. This paper, concentrates on a new formulation cost-sensitive classification considering both misclassification cost and accuracy measures. Also, in the training phase a new heuristic algorithm will be used to solve the proposed model. The superiority of the novel method is affirmed after comparing to the traditional ones. Cost-sensitive Learning Classification Support Vector Machine Supervised Learning https://www.jise.ir/article_59552_4c92882e8099daaddaa333258b4c30fc.pdf