References
- Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30, 1145-1159. https://doi.org/10.1016/S0031-3203(96)00142-2
- Dreiseitl, S., Ohno-Machado, L. and Binder, M. (2000). Comparing three-class diagnostic tests by three-way ROC analysis, Medical Decision Making, 20, 323-331. https://doi.org/10.1177/0272989X0002000309
- Egan, J. P. (1975). Signal Detection Theory and ROC Analysis, Academic Press, New York.
- Engelmann, B., Hayden, E. and Tasche, D. (2003). Testing rating accuracy, Risk, 82-86.
- Fawcett, T. (2003). ROC graphs: notes and practical considerations for data mining researchers, Available from: http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf.
- Hanley, J. A. and McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29-36. https://doi.org/10.1148/radiology.143.1.7063747
- Heckerling, P. S. (2001). Parametric three-way receiver operating characteristic surface analysis using mathematica, Medical Decision Making, 21, 409-417. https://doi.org/10.1177/02729890122062703
- Hong, C. S. (2009). Optimal threshold from ROC and CAP curves, Communications in Statistics - Stimulation and Computation, 38, 2060-2072. https://doi.org/10.1080/03610910903243703
- Hong, C. S. and Choi, J.S. (2009). Optimal thre shold from ROC and CAP curves, The Korean Journal of Applied Statistics, 22, 911-921. https://doi.org/10.5351/KJAS.2009.22.5.911
- Hong, C. S., Joo, J. S. and Choi, J. S. (2010). Optimal thresholds from mixture distributions, The Korean Journal of Applied Statistics, 23, 13-28. https://doi.org/10.5351/KJAS.2010.23.1.013
- Hong, C. S. and Jung, D. G. (2014). Standard criterion of hypervolume under the ROC manifold, Journal of the Korean Data & Information Science Society, 25, 473-483. https://doi.org/10.7465/jkdi.2014.25.3.473
- Hong, C. S., Jung, E. S. and Jung, D. G. (2013). Standard criterion of VUS for ROC surface, The Korean Journal of Applied Statistics, 26, 977-985. https://doi.org/10.5351/KJAS.2013.26.6.977
- Li, J. and Fine, J. P. (2008). ROC analysis with multiple classes and multiple tests: Methodology and its application in microarray studies, Biostatistics, 9, 566-576. https://doi.org/10.1093/biostatistics/kxm050
- Mossman, D. (1999). Three-way ROCs, Medical Decision Making, 19, 78-89. https://doi.org/10.1177/0272989X9901900110
- Nakas, C. T., Alonzo, T. A. and Yiannoutsos, C. T. (2010). Accuracy and cut-off point selection in three class classification problems using a generalization of the Youden index, Statistics in Medicine, 29, 2946-2955. https://doi.org/10.1002/sim.4044
- Nakas, C. T. and Yiannoutsos, C. T. (2004). Ordered multiple-class ROC analysis with continuous measurements, Statistics in Medicine, 23, 3437-3449. https://doi.org/10.1002/sim.1917
- Patel, A. C. and Markey, M. K. (2005). Comparison of three-class classification performance metrics:A case study in breast cancer CAD, Proceedings of SPIE, 5749, 581-589.
- Provost, F. and Fawcett, T. (2001). Robust classification for imprecise environments, Machine Learning, 42, 203-231. https://doi.org/10.1023/A:1007601015854
- Rosset, S. (2004). Model Selection via the AUC, In Proceedings of the 21st International Conference on Machine Learning (ICML'04), Banff, Canada, 89.
- Scureld, B. K. (1996). Multiple-event forced-choice tasks in the theory of signal detectability, Journal of Mathematical Psychology, 40, 253-269. https://doi.org/10.1006/jmps.1996.0024
- Sobehart, J. and Keenan, S. (2001). Measuring default accurately, Risk - Credit Risk Special Report, 14, 31-33.
- Swets, J. A. (1988). Measuring the accuracy of diagnostic systems, Science, 240, 1285-1293. https://doi.org/10.1126/science.3287615
- Swets, J. A., Dawes, R. M. and Monahan, J. (2000). Better decisions through science, Scientific American, 283, 82-87.
- Wandishin, M. S. and Mullen, S. J. (2009). Multiclass ROC analysis, Weather and Forecasting, 24, 530-547. https://doi.org/10.1175/2008WAF2222119.1
- Zou, K. H., O'Malley, A. J. and Mauri, L. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models, Circulation, 115, 654-657. https://doi.org/10.1161/CIRCULATIONAHA.105.594929
Cited by
- Proposition of polytomous discrimination index and test statistics vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.337
- Parameter estimation for the imbalanced credit scoring data using AUC maximization vol.29, pp.2, 2016, https://doi.org/10.5351/KJAS.2016.29.2.309
- Standardized polytomous discrimination index using concordance vol.27, pp.1, 2016, https://doi.org/10.7465/jkdi.2016.27.1.33
- Test Statistics for Volume under the ROC Surface and Hypervolume under the ROC Manifold vol.22, pp.4, 2015, https://doi.org/10.5351/CSAM.2015.22.4.377