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Alternative Optimal Threshold Criteria: MFR

대안적인 분류기준: 오분류율곱

  • Received : 2014.08.12
  • Accepted : 2014.09.30
  • Published : 2014.10.31

Abstract

We propose the multiplication of false rates (MFR) which is a classification accuracy criteria and an area type of rectangle from ROC curve. Optimal threshold obtained using MFR is compared with other criteria in terms of classification performance. Their optimal thresholds for various distribution functions are also found; consequently, some properties and advantages of MFR are discussed by comparing FNR and FPR corresponding to optimal thresholds. Based on general cost function, cost ratios of optimal thresholds are computed using various classification criteria. The cost ratios for cost curves are observed so that the advantages of MFR are explored. Furthermore, the de nition of MFR is extended to multi-dimensional ROC analysis and the relations of classification criteria are also discussed.

본 연구는 ROC 곡선에서 형성되는 면적 형태로 나타나는 분류정확도기준인 오분류율곱(multiplication of false rates; MFR)를 제안한다. MFR 기준과 다른 기준로부터 구한 최적분류점의 분류성과에 대하여 비교 분석한다. 다양한 분포함수에 대하여 최적분류점을 구하고 이에 대응하는 FNR과 FPR을 비교하면서 MFR의 특징과 장점을 유도한다. 일반적인 비용함수를 바탕으로 분류점에 대한 비용비율을 다양한 분류기준을 이용하여 구한다. 비용곡선에 대한 비용비율의 관계를 정리하여 MFR 기준의 장점을 탐색한다. MFR 기준의 정의를 다차원 ROC 분석으로 확장하고 다차원의 다른 분류기준과의 관계를 설명하면서 토론한다.

Keywords

References

  1. Adams, N. M. and Hand, D. J. (1999). Comparing classifiers when the misallocation costs are uncertain, Pattern Recognition, 30, 1139-1147.
  2. 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.
  3. Brasil, P. (2010). Diagnostic test accuracy evaluation for medical professionals, Package DiagnosisMed in R.
  4. 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.
  5. 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
  6. 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.
  7. 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.
  8. 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
  9. Engelmann, B., Hayden, E. and Tasche, D. (2003). Measuring the discriminative power of rating systems, Discussion paper, Series 2: Banking and Financial Supervision, Frankfurt.
  10. 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.
  11. Freeman, E. A. and Moisen, G. (2008). PresenceAbsence: An R package for presence absence analysis, Journal of Statistical Software, 23 1-31.
  12. 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
  13. 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.
  14. 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.
  15. 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
  16. Holte, R. C. and Drummond, C. (2008). Cost-sensitive classifier evaluation using cost curves, Advances in Knowledge discovery and Data Mining, 5012, 26-29
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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.
  25. 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
  26. 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
  27. 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.
  28. Liu, Z., Tan, M. and Jiang, F. (2009). Regularized F-measure maximization for feature selection and classification, Journal of Biomedicine and Biotechnology, 617946.
  29. Lambert, J. and Lipkovich, I. (2008). A macro for getting more out of your ROC curve, SAS Global forum, paper 231, Indianapolis.
  30. Metz, C. E. (1978). Basic principles of ROC analysis, Seminars in Nuclear Medicine, 8, 283-298.
  31. Pepe, M. S. (2003). The statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press, Oxford.
  32. 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.
  33. Sobehart, J. and Keenan, S. C. (2001). Measuring default accurately, Credit Risk Special Report, Risk, 14, 31-33.
  34. Tasche, D. (2006). Validation of internal rating systems and PD estimates, arXiv.org, eprint arXiv: physics/0606071, Frankfurt.
  35. 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.
  36. 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
  37. Vuk, M. and Curk, T. (2006). ROC curve, lift chart and calibration plot, Metodoloki zvezki, 3, 89-108
  38. 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
  39. Zhou, X. H., Obuchowski, N. A. and McClish, D. K. (2002). Statistical Methods in Diagnostic Medicine, Wiley, New York.