참고문헌
- Adams, N. M. and Hand, D. J. (1999). Comparing classifiers when the misallocation costs are uncertain, Pattern Recognition, 30, 1139-1147.
- Antonie, M. L., Zaiane, O. R. and Holte, R. C. (2006). Learning to use a learned model: A two-stage approach to classification, Proceedings of the 6th IEEE International Conference on Data Mining(ICDM'06), 33-42.
- Brasil, P. (2010). Diagnostic test accuracy evaluation for medical professionals, Package DiagnosisMed in R.
- Briggs, W. M. and Zaretzki, R. (2007). The skill plot: A graphical technique for the evaluating the predictive usefulness of continuous diagnostic tests, Biometrics, 63, 250-261.
- Cantor, S. B., Sun, C. C., Tortolero-Luna, G., Richards-Korturn, R. and Follen, M. (1999). A comparison of CB ratios from studies using receiver operating characteristic curve analysis, Journal of Clinical Epidemiology, 52, 885-892. https://doi.org/10.1016/S0895-4356(99)00075-X
- Davis, J. and Goadrich, M. (2006). The relationship between precision-recall and ROC curves, Proceedings of the 23rd International Conference on Machine Learning(ICML'06), 233-240.
- Drummond, C. and Holte, R. (2000). Explicitly representing expected cost: An alternative to ROC repre- sentation, Technical Report, School of Information Technology and Engineering, University of Ottawa.
- Drummond, C. and Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance, Machine Learning, 65, 95-130. https://doi.org/10.1007/s10994-006-8199-5
- Engelmann, B., Hayden, E. and Tasche, D. (2003). Measuring the discriminative power of rating systems, Discussion paper, Series 2: Banking and Financial Supervision, Frankfurt.
- Fawcett, T. (2003). ROC Graphs: Notes and practical considerations for data mining researchers, Technical Report HPL-2003-4, HP Laboratories Palo Alto, 1-28, Palo Alto.
- Freeman, E. A. and Moisen, G. (2008). PresenceAbsence: An R package for presence absence analysis, Journal of Statistical Software, 23 1-31.
- Greiner, M. and Gardner, I. A. (2000). Epidemiologic issues in the validation of veterinary diagnostic tests, Preventive Veterinary Medicine, 45, 3-22. https://doi.org/10.1016/S0167-5877(00)00114-8
- Hand, D. J. (2009). Mismatched models, wrong results, and dreadful decisions: On choosing appropriate data mining tools, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining.
- Hand, D. J. and Zhou, F. (2009). Evaluating models for classifying customers in retail banking collections, Journal of the Operational Society, DOI: 10.1057/jors.2009.129, London.
- Hilden, J. and Glasziou, P. (1996). Regret graphs, diagnostic uncertainty and Youden's index, Statistics in Medicine, 15, 969-986. https://doi.org/10.1002/(SICI)1097-0258(19960530)15:10<969::AID-SIM211>3.0.CO;2-9
- Holte, R. C. and Drummond, C. (2008). Cost-sensitive classifier evaluation using cost curves, Advances in Knowledge discovery and Data Mining, 5012, 26-29
- Hong, C. S. (2009). Optimal threshold from ROC and CAP curves, Communications in Statistics-Simulation and Computation, 38, 2060-2072. https://doi.org/10.1080/03610910903243703
- Hong, C. S. and Lee W. Y. (2011). ROC curve fitting with normal mixture, The Korean Journal of Applied Statistics, 24, 269-278. https://doi.org/10.5351/KJAS.2011.24.2.269
- Hong, C. S. and Yoo, H. S. (2010). Cost ratios for cost and ROC curves, Communications of The Korean Statistical Society, 17, 755-765. https://doi.org/10.5351/CKSS.2010.17.6.755
- Hong, C. S. and Joo, J. S. (2010). Optimal thresholds from non-normal mixture, The Korean Journal of Applied Statistics, 23, 943-953. https://doi.org/10.5351/KJAS.2010.23.5.943
- 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., Lin, M. H. and Hong, S.W. (2011). ROC function estimation, The Korean Journal of Applied Statistics, 24, 987-994. https://doi.org/10.5351/KJAS.2011.24.6.987
- 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
- Hoshino, R., Coughtrey, D., Sivaraja, S., Volnyansky, I., Auer, S. and Trishtchenko, A. (2009). Applications and extensions of cost curves to marine container inspection, Annals of Operations Research, DOI: 10.1007/s10479-009-0669-2.
- Jund, J., Rabillous, M., Wallon, M. and Ecochard, R. (2005). Methods to estimate the optimal threshold for normally or log-normally distributed biological tests, Medical Decision Making, 25, 406-415. https://doi.org/10.1177/0272989X05276855
- Kim, J. H. (2004). Roc and cost graphs for general cost matrix where correct classification incur non-zero costs, Communications of the Korean Statistical Society, 11, 21-30. https://doi.org/10.5351/CKSS.2004.11.1.021
- Liu, Y. and Shriberg, E. (2007). Comparing evaluation metrics for sentence boundary detection, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP'07), 4, 185-188.
- Liu, Z., Tan, M. and Jiang, F. (2009). Regularized F-measure maximization for feature selection and classification, Journal of Biomedicine and Biotechnology, 617946.
- Lambert, J. and Lipkovich, I. (2008). A macro for getting more out of your ROC curve, SAS Global forum, paper 231, Indianapolis.
- Metz, C. E. (1978). Basic principles of ROC analysis, Seminars in Nuclear Medicine, 8, 283-298.
- Pepe, M. S. (2003). The statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, Oxford.
- Provost, F. and Fawcett, T. (1997). Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, 43-48.
- Sobehart, J. and Keenan, S. C. (2001). Measuring default accurately, Credit Risk Special Report, Risk, 14, 31-33.
- Tasche, D. (2006). Validation of internal rating systems and PD estimates, arXiv.org, eprint arXiv: physics/0606071, Frankfurt.
- Turney, P. D. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research, 2, 369-409.
- Velez, D. R., White, B. C., Motsinger, A. A., Bush, W. S., Ritichie, M. D., Williams, S. M. and Moore, J. H. (2007). A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction, Genetic Epidemiology, 31, 306-315. https://doi.org/10.1002/gepi.20211
- Vuk, M. and Curk, T. (2006). ROC curve, lift chart and calibration plot, Metodoloki zvezki, 3, 89-108
- Yoo, H. S. and Hong, C. S. (2011). Optimal criterion of classification accuracy measures for normal mixture, Communications of The Korean Statistical Society, 18, 343-355. https://doi.org/10.5351/CKSS.2011.18.3.343
- Zhou, X. H., Obuchowski, N. A. and McClish, D. K. (2002). Statistical Methods in Diagnostic Medicine, Wiley, New York.