Twin SVM with a reject option through ROC curve
Zhang, Jing Bo
Date of Issue2017
School of Electrical and Electronic Engineering
Nanyang Environment and Water Research Institute
This paper proposes a new method which embeds a reject option in twin support vector machine (RO-TWSVM) through the Receiver Operating Characteristic (ROC) curve for binary classification. The proposed RO-TWSVM enhances the classification robustness through inclusion of an effective rejection rule for potentially misclassified samples. The method is formulated based on a cost-sensitive framework which follows the principle of minimization of the expected cost of classification. Extensive experiments are conducted on synthetic and real-world data sets to compare the proposed RO-TWSVM with the original TWSVM without a reject option (TWSVM-without-RO) and the existing SVM with a reject option (RO-SVM). The experimental results demonstrate that our RO-TWSVM significantly outperforms TWSVM-without-RO, and in general, performs better than RO-SVM.
Journal of the Franklin Institute
© 2017 The Franklin Institute (published by Elsevier). This is the author created version of a work that has been peer reviewed and accepted for publication in Journal of the Franklin Institute, published by Elsevier Ltd. on behalf of The Franklin Institute. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.jfranklin.2017.05.003].