Twin SVM with a reject option through ROC curve
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 f...
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sg-ntu-dr.10356-868712020-09-26T22:03:41Z Twin SVM with a reject option through ROC curve Lin, Dongyun Sun, Lei Toh, Kar-Ann Zhang, Jing Bo Lin, Zhiping School of Electrical and Electronic Engineering Nanyang Environment and Water Research Institute ROC Curves Twin SVM 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. Accepted version 2018-01-03T04:55:54Z 2019-12-06T16:30:38Z 2018-01-03T04:55:54Z 2019-12-06T16:30:38Z 2017 Journal Article Lin, D., Sun, L., Toh, K.-A., Zhang, J. B., & Lin, Z. (2017). Twin SVM with a reject option through ROC curve. Journal of the Franklin Institute, 355(4), 1710-1732. 0016-0032 https://hdl.handle.net/10356/86871 http://hdl.handle.net/10220/44245 10.1016/j.jfranklin.2017.05.003 en 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]. 25 p. application/pdf |
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ROC Curves Twin SVM Lin, Dongyun Sun, Lei Toh, Kar-Ann Zhang, Jing Bo Lin, Zhiping Twin SVM with a reject option through ROC curve |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Lin, Dongyun Sun, Lei Toh, Kar-Ann Zhang, Jing Bo Lin, Zhiping |
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Article |
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Lin, Dongyun Sun, Lei Toh, Kar-Ann Zhang, Jing Bo Lin, Zhiping |
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Lin, Dongyun |
title |
Twin SVM with a reject option through ROC curve |
title_short |
Twin SVM with a reject option through ROC curve |
title_full |
Twin SVM with a reject option through ROC curve |
title_fullStr |
Twin SVM with a reject option through ROC curve |
title_full_unstemmed |
Twin SVM with a reject option through ROC curve |
title_sort |
twin svm with a reject option through roc curve |
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2018 |
url |
https://hdl.handle.net/10356/86871 http://hdl.handle.net/10220/44245 |
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1681059731785908224 |