Abstract
The performance of a reject option classifiers is quantified using \(0-d-1\) loss where \(d \in (0,.5)\) is the loss for rejection. In this paper, we propose double ramp loss function which gives a continuous upper bound for \((0-d-1)\) loss. Our approach is based on minimizing regularized risk under the double ramp loss using difference of convex programming. We show the effectiveness of our approach through experiments on synthetic and benchmark datasets. Our approach performs better than the state of the art reject option classification approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
An, L.T.H., Tao, P.D.: Solving a class of linearly constrained indefinite quadratic problems by d.c. algorithms. Journal of Global Optimization 11, 253–285 (1997)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Bartlett, P.L., Wegkamp, M.H.: Classification with a reject option using a hinge loss. Journal of Machine Learning Research 9, 1823–1840 (2008)
Chow, C.K.: On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16(1), 41–46 (1970)
Fumera, G., Roli, F.: Support Vector Machines with Embedded Reject Option. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol. 2388, pp. 68–82. Springer, Heidelberg (2002)
Ghosh, A., Manwani, N., Sastry, P.S.: Making risk minimization tolerant to label noise. CoRR, abs/1403.3610 (2014
Grandvalet, Y., Rakotomamonjy, A., Keshet, J., Canu, S.: Support vector machines with a reject option. In: NIPS, pp. 537–544 (2008)
Herbei, R., Wegkamp, M.H.: Classification with reject option. The Canadian Journal of Statistics 34(4), 709–721 (2006)
Karatzoglou, A., Smola, A., Hornik, K., Zeileis, A.: kernlab - an S4 package for kernel methods in R. Journal of Statistical Software 11(9), 1–20 (2004)
Manwani, N., Sastry, P.S.: Noise tolerance under risk minimization. IEEE Transactions on Systems, Man and Cybernetics: Part-B, 43, 1146–1151 (2013)
Ong, C.S., An, L.T.H.: Learning sparse classifiers with difference of convex functions algorithms. Optimization Methods and Software (ahead-of-print), 1–25 (2012)
Wegkamp, M., Yuan, M.: Support vector machines with a reject option. Bernaulli 17(4), 1368–1385 (2011)
Yuan, M., Wegkamp, M.: Classification methods with reject option based on convex risk minimization. Journal of Machine Learning Research 11, 111–130 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Manwani, N., Desai, K., Sasidharan, S., Sundararajan, R. (2015). Double Ramp Loss Based Reject Option Classifier. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-18038-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18037-3
Online ISBN: 978-3-319-18038-0
eBook Packages: Computer ScienceComputer Science (R0)