Estimating the ROC Curve and Its Significance for Classification Models' Assessment

• Autorzy: Krzysztof Gajowniczek , Tomasz Ząbkowski , Ryszard Szupiluk
• Języki publikacji: EN

Abstrakty

EN

Article presents a ROC (receiver operating characteristic) curve and its application for classification models' assessment. ROC curve, along with area under the receiver operating characteristic (AUC) is frequently used as a measure for the diagnostics in many industries including medicine, marketing, finance and technology. In this article, we discuss and compare estimation procedures, both parametric and non-parametric, since these are constantly being developed, adjusted and extended. (original abstract)

Słowa kluczowe

EN

Classification methods; Estimation

PL

Metody klasyfikacyjne; Estymacja

• Czasopismo: Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego
• Rocznik: 2014
• Tom: 15(XV)
• Numer: nr 2
• Strony: 382--391

Twórcy

autor

Krzysztof Gajowniczek

• Warsaw University of Life Sciences - SGGW, Poland

autor

Tomasz Ząbkowski

• Warsaw University of Life Sciences - SGGW, Poland

autor

Ryszard Szupiluk

• Warsaw School of Economics, Poland

Bibliografia

• Bradley A.P. (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, vol. 30, No. 7, pp. 1145-1159.
• Calders T., Jaroszewicz S. (2007) Efficient AUC Optimization for Classification, Proceedings of The 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'07), pp. 42-53.
• Faraggi D., Reiser B. (2002) Estimation of the area under the ROC curve, Statistics in Medicine, vol. 21, pp. 3093-3096.
• Gajowniczek K., Ząbkowski T. (2012) Problemy modelowania rezygnacji klientów w telefonii komórkowej, Metody Ilościowe w Badaniach Ekonomicznych, vol. 13, No 3, pp. 65-79.
• Hanley J. A., McNeil B. J. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology vol. 143, pp. 29-36.
• Hanley J. A., McNeil B. J. (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology vol. 148, pp. 839-843.
• Krzyśko M., Wołyński W., Górecki T., Skorzybut M. (2008) Systemy uczące się, Wydawnictwo Naukowo-Techniczne.
• Lloyd C. J. (1998) Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems, Journal of the American Statistical Association, vol. 93, pp. 1356-1364.
• Mann H. B., Whitney D. R. (1947) On a test of whether one of two random variables is stochastically larger than the other, The Annals of Mathematical Statistics; vol. 18, pp. 50-60.
• Neslin S. (2002) Cell2Cell: The churn game. Cell2Cell Case Notes, Hanover, NH: Tuck School of Business, Dartmoth College, Downloaded from: http://www.fuqua.duke.edu/centers/ccrm/datasets/cell/
• Youden W. J. (1950) An index for rating diagnostic tests, Cancer, vol.3, pp. 32-35.
• Zou K. H.; Hall W. J., Shapiro D. E. (1997). Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests, Statistics in Medicine, vol. 16, pp. 2143-2156.
• Zou K. H., Hall W.J. (2000) Two transformation models for estimating an ROC curve derived from continuous data, Journal of Applied Statistics, vol. 27, pp. 621-631.