Abstract
It is known that RankSVM can optimize area under the ROC curve (AUC) for binary classification by maximizing the margin between the positive class and the negative class. Since the objective function of Siamese Network for rank learning is the same as RankSVM, Siamese Network can also optimize AUC for binary classification. This paper proposes a method for binary classification by combining Siamese Network for rank learning with logistic regression. The effectiveness is investigated by comparing the AUC scores of the proposed method with the standard Convolutional Neural Network. Then the proposed method is extended to multi-class classification problem by using Siamese Network and multinominal logistic regression. We extend the proposed binary classifier to multi-class classification by using one-vs-others approach.
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References
Vapnik, V., Lerner, A.: A pattern recognition using generalized portrait method. Autom. Remote Control 24(6), 774–780 (1963)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. In: Smola, B., Schoelkopf, S. (eds.) Advances in Large Margin Classifiers. MIT Press, Cambridge (2000)
Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with SVMs. Inf. Retr. 13, 201–215 (2010)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Conference Neural Information Processing Systems, pp. 1097–1105 (2012)
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a Siamese time delay neural network. In: Advances in Neural Information Processing Systems, NIPS 1993, vol. 6 (1993)
Chopra, S., Hadsell, R. LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 539–546 (2005)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2006, vol. 2, pp. 1735–1742 (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR 2016 (2016)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of ICCV 2017 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
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This work was partly supported by JSPS KAKENHI Grant Number 16K00239.
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Oki, H., Miyao, J., Kurita, T. (2019). Siamese Network for Classification with Optimization of AUC. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_27
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DOI: https://doi.org/10.1007/978-3-030-36711-4_27
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