Browse > Article

Classification accuracy measures with minimum error rate for normal mixture  

Hong, C.S. (Department of Statistics, Sungkyunkwan University)
Lin, Meihua (Research Institute of Applied Statistics, Sungkyunkwan University)
Hong, S.W. (Research Institute of Applied Statistics, Sungkyunkwan University)
Kim, G.C. (Research Institute of Applied Statistics, Sungkyunkwan University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.4, 2011 , pp. 619-630 More about this Journal
Abstract
In order to estimate an appropriate threshold and evaluate its performance for the data mixed with two different distributions, nine kinds of well-known classification accuracy measures such as MVD, Youden's index, the closest-to- (0,1) criterion, the amended closest-to- (0,1) criterion, SSS, symmetry point, accuracy area, TA, TR are clustered into five categories on the basis of their characters. In credit evaluation study, it is assumed that the score random variable follows normal mixture distributions of the default and non-default states. For various normal mixtures, optimal cut-off points for classification measures belong to each category are obtained and type I and II error rates corresponding to these cut-off points are calculated. Then we explore the cases when these error rates are minimized. If normal mixtures might be estimated for these kinds of real data, we could make use of results of this study to select the best classification accuracy measure which has the minimum error rate.
Keywords
Accuracy; discrimination; error; threshold;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Brasil, P. (2010). Diagnostic test accuracy evaluation for medical professionals, Package 'DiagnosisMed' in R.
2 Cantor, S. B., Sun, C. C., Tortolero-Luna, G., Richards-Kortum, R. and Follen, M. (1999). A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. Journal of Clinical Epidemiology, 52, 885-892.   DOI   ScienceOn
3 Connell, F. A. and Koepsell, T. D. (1985). Measures of gain in certainty from a diagnostic test. American Journal of Epidemiology, 121, 744-753.   DOI   ScienceOn
4 Freeman, E. A. and Moisen, G. G. (2008). A comparison of theperformance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling, 217, 48-58.   DOI   ScienceOn
5 Greiner, M. M. and Gardner, I. A. (2000). Epidemiologic issues in the validation of veterinary diagnostic tests. Preventive Veterinary Medicine, 45, 3-22.   DOI   ScienceOn
6 Krzanowski, W. J. and Hand, D. J. (2009). ROC curves for continuous data, Champman & Hall/CRC, Boca Raton, FL.
7 Liu, C., White, M. and Newell1, G. (2009). Measuring the accuracy of species distribution models: A review. 18th World IMACS/MODSIM Congress. http://mssanz.org.au/modsim09.
8 Lambert, J. and Lipkovich, I. (2008). A macro for getting more out of your ROC curve. SAS Global Forum, 231.
9 Moses, L. E., Shapiro, D. and Littenberg, B. (1993). Combining independent studies of a diagnostic test into a summary ROC curve: Data-analytic approaches and some additional considerations. Statistics in Medicine, 12, 1293-1316.   DOI   ScienceOn
10 Perkins, N. J. and Schisterman, E. F. (2006). The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. American Journal of Epidemiology, 163, 670-675.   DOI   ScienceOn
11 Pepe, M. S. (2003). The statistical evaluation of medical tests for classification and prediction, University Press, Oxford. .
12 Velez, D. R., White, B. C., Motsinger, A. A., Bush, W. S., Ritchie, 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.   DOI   ScienceOn
13 Youden, W. J. (1950). Index for rating diagnostic test. Cancer, 3, 32-35.   DOI   ScienceOn
14 홍종선, 권태완 (2010). 수익률분포의 적합과 리스크값 추정. <한국데이터정보과학회지>, 21, 219-229.
15 홍종선, 이원용 (2011). 정규혼합분포를 이용한 ROC 분석. <응용통계연구>, 24, 269-278.
16 홍종선, 주재선, 최진수 (2010). 혼합분포에서 최적분류점. <응용통계연구>, 23, 13-28.