References
- Bartlett, P., Jordan, M. I., and McAuliffe, J. D. (2006), “Convexity, Classification, and Risk Bound,” Journal of the American Statistical Association, 101, 138–156. DOI: https://doi.org/10.1198/016214505000000907.
- Bi, Y., and Jeske, D. R. (2010), “The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis Under Class-Conditional Classification Noise,” Journal of Multivariate Analysis, 101, 1622–1637. DOI: https://doi.org/10.1016/j.jmva.2010.03.001.
- Bootkrajang, J. (2016), “A Generalised Label Noise Model for Classification in the Presence of Annotation Errors,” Neurocomputing, 192, 61–71. DOI: https://doi.org/10.1016/j.neucom.2015.12.106.
- Bootkrajang, J., and Kabán, A. (2012), “Label-Noise Robust Logistic Regression and Its Applications,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 143–158.
- Carroll, R. J., and Peterson, S. (1993), “On Robustness in the Logistic Regression Model,” Journal of the Royal Statistical Society, Series B, 55, 693–706. DOI: https://doi.org/10.1111/j.2517-6161.1993.tb01934.x.
- Chhikara, R. S., and McKeon, J. (1984), “Linear Discriminant Analysis With Misallocation in Training Samples,” Journal of the American Statistical Association, 79, 899–906. DOI: https://doi.org/10.1080/01621459.1984.10477109.
- Dua, D., and Graff, C. (2019), “UCI Machine Learning Repository,” University of California, School of Information and Computer Science, Irvine, CA, available at http://archive.ics.uci.edu/ml.
- Frénay, B., and Verleysen, M. (2014), “Classification in the Presence of Label Noise: A Survey,” IEEE Transactions on Neural Networks and Learning Systems, 25, 845–869. DOI: https://doi.org/10.1109/TNNLS.2013.2292894.
- Lachenbruch, P. A. (1966), “Discriminant Analysis When the Initial Samples Are Misclassified,” Technometrics, 8, 657–662. DOI: https://doi.org/10.2307/1266637.
- Lachenbruch, P. A. (1974), “Discriminant Analysis When the Initial Samples Are Misclassified II: Non-Random Misclassification Models,” Technometrics, 16, 419–424.
- Lachenbruch, P. A. (1979), “Note on Initial Misclassification Effects on the Quadratic Discriminant Function,” Technometrics, 21, 129–132.
- Lee, S., Shin, H., and Lee, S. H. (2016), “Label-Noise Resistant Logistic Regression for Functional Data Classification With an Application to Alzheimer’s Disease,” Biometrics, 72, 1325–1335. DOI: https://doi.org/10.1111/biom.12504.
- Lee, Y., MacEachern, S. N., and Jung, Y. (2012), “Regularization of Case-Specific Parameters for Robustness and Efficiency,” Statistical Science, 27, 350–372. DOI: https://doi.org/10.1214/11-STS377.
- Lin, Y. (2004), “A Note on Margin-Based Loss Functions in Classification,” Statistics and Probability Letters, 68, 73–82. DOI: https://doi.org/10.1016/j.spl.2004.03.002.
- Liu, Y., and Shen, X. (2006), “Multicategory ψ-Learning,” Journal of the American Statistical Association, 101, 500–509. DOI: https://doi.org/10.1198/016214505000000781.
- Manwani, N., and Sastry, P. S. (2013), “Noise Tolerance Under Risk Minimization,” IEEE Transactions on Cybernetics, 43, 1146–1151. DOI: https://doi.org/10.1109/TSMCB.2012.2223460.
- Maronna, R. A., Margin, R. D., and Yohai, V. J. (2006), Robust Statistics: Theory and Methods, New York: Wiley.
- Park, S. Y., and Liu, Y. (2011), “Robust Penalized Logistic Regression With Truncated Loss Function,” Canadian Journal of Statistics, 39, 300–323. DOI: https://doi.org/10.1002/cjs.10105.
- Pitropakis, N., Panaousis, E., Giannetsos, T., Anastasiadis, E., and Loukas, G. (2019), “A Taxonomy and Survey of Attacks Against Machine Learning,” Computer Science Review, 34, 100199. DOI: https://doi.org/10.1016/j.cosrev.2019.100199.
- Pregibon, D. (1982), “Resistant Fits for Some Commonly Used Logistic Models With Medical Applications,” Biometrics, 38, 485–498. DOI: https://doi.org/10.2307/2530463.
- Shen, X., Tseng, G. C., Zhang, X., and Wong, W. H. (2003), “On ψ-Learning,” Journal of the American Statistical Association, 98, 724–734. DOI: https://doi.org/10.1198/016214503000000639.
- Tibshirani, J., and Manning, D. C. (2014), “Robust Logistic Regression Using Shift Parameters,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Vol. 2), pp. 124–129.
- Wang, L., Zhu, J., and Zou, H. (2008), “Hybrid Huberized Support Vector Machines for Microarray Classification and Gene Selection,” Bioinformatics, 24, 412–419. DOI: https://doi.org/10.1093/bioinformatics/btm579.
- Wu, Y., and Liu, Y. (2007), “Robust Truncated Hinge Loss Support Vector Machines,” Journal of the American Statistical Association, 102, 974–983. DOI: https://doi.org/10.1198/016214507000000617.
- Zhu, J., and Hastie, T. (2005), “Kernel Logistic Regression and the Import Vector Machine,” Journal of Computational and Graphical Statistics, 14, 185–205. DOI: https://doi.org/10.1198/106186005X25619.