ROC curve equivalence using the Kolmogorov-Smirnov test

Pattern Recognition Letters

Volumn 34, Issue 5, 2013, Pages 470-475

  Bradley, Andrew P ^a  

^a UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

AUC; Coherence; KS test; ROC curves; Sensitivity; Specificity

Indexed keywords

AUC; KS-TEST; ROC CURVES; SENSITIVITY; SPECIFICITY;

COHERENT LIGHT; LEARNING ALGORITHMS;

TESTING;

EID: 84872800974     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2012.12.021     Document Type: Article

References (19)

1. J. Antoch, L. Prchal, and P. Sarda Nonparametric comparison of ROC curves: Testing equivalence Nonparametrics Robustness Modern Statist. Inference Time Ser. Anal. 7 2010 12 24
2. D. Berrar, and P. Flach Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them) Briefings Bioinform. 13 1 2012 83 97
3. A.P. Bradley ROC curves and the X2 test Pattern Recognition Lett. 17 3 1996 287 294
4. A.P. Bradley The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recognition 30 7 1997 1145 1159 (Pubitemid 127406521)
5. Bradley, A.P., Longstaff, I., 2004. Sample size estimation using the receiver operating characteristic curve. In: Proceedings 17th International Conference on Pattern Recognition, vol. 4, pp. 428-431.
6. G. Campbell Advances in statistical methodology for the evaluation of diagnostic and laboratory tests Statist. Med. 13 5-7 1994 499 508 (Pubitemid 24106732)
7. C. Drummond, and R.C. Holte Cost curves: An improved method of visualising classifier performance Machine Learn. 65 2006 95 130 (Pubitemid 44451195)
8. T. Fawcett An introduction to ROC analysis Pattern Recognition Lett. 27 8 2006 861 874
9. P. Flach, J. Hernandez-Orallo, and C. Ferri A coherent interpretation of AUC as a measure of aggregated classification performance L. Getoor, T. Scheffer, Proc. 28th Internat. Conf. on Machine Learning (ICML-11), ICML '11 2011 ACM New York, NY, USA 657 664
10. D.J. Hand Good practice in retail credit scorecard assessment J. Oper. Res. Soc. 56 2005 1109 1117 (Pubitemid 41230445)
11. D.J. Hand Measuring classifier performance: a coherent alternative to the area under the ROC curve Machine Learn. 77 2009 103 123
12. D.J. Hand, and C. Anagnostopoulos When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance? 2012 Pattern Recognition Lett.
13. J. Hilden The area under the ROC curve and its competitors Med. Decision Making 11 2 1991 95 101
14. W.J. Krzanowski, and D.J. Hand Testing the difference between two kolmogorovsmirnov values in the context of receiver operating characteristic curves J. Appl. Statist. 38 3 2011 437 450
15. Landgrebe, T.C., Paclik, P., Duin, R.P., Bradley, A.P., 2006. Precision-recall operating characteristic (P-ROC) curves in imprecise environments In: Proc. 18th Internat. Conf. on Pattern Recognition, vol. 4, pp. 123-127. (Pubitemid 46553378)
16. Macskassy, S., Provost, F., 2004. Confidence bands for ROC curves: Methods and an empirical study. In: Proc. First Workshop on ROC Analysis in AI.
17. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery Numerical Recipes: The Art of Scientific Computing 3rd ed. 2007 Cambridge University Press
18. J.A. Swets, R.M. Dawes, and J. Monahan Better Decisions Through Science 2000 Scientific American pp. 82-87
19. E.S. Venkatraman, and C.B. Begg A distribution-free procedure for comparing receiver operating characteristic curves from a paired experiment Biometrika 83 4 1996 835 848