Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Receiver Operating Characteristic

  • Pang-Ning TanEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_569


Operating characteristic; Relative operating characteristic; ROC


Receiver operating characteristic (ROC) analysis is a graphical approach for analyzing the performance of a classifier. It uses a pair of statistics – true positive rate and false positive rate – to characterize a classifier’s performance. The statistics are plotted on a two-dimensional graph, with false positive rate on the x-axis and true positive rate on the y-axis. The resulting plot can be used to compare the relative performance of different classifiers and to determine whether a classifier performs better than random guessing.

Historical Background

ROC analysis was originally developed in signal detection theory to deal with the problem of discriminating known signals from a random noise background [11]. It was first applied to the radar detection problem to quantify how effective targets such as enemy aircrafts can be identified according to their radar signatures. In the 1960s, ROC analysis...

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Recommended Reading

  1. 1.
    Cortes C, Mohri M. Auc optimization vs. error rate minimization. In: Advances in Neural Information Proceedings of the Systems 16, Proceedings of the Neural Information Proceedings of the Systems; 2003.Google Scholar
  2. 2.
    Davis J, Goadrich M. The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning; 2006.Google Scholar
  3. 3.
    Drummond C, Holte RC. Explicitly representing expected cost: an alternative to roc representation. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000. p. 198–207.Google Scholar
  4. 4.
    Fawcett T. An introduction to roc analysis. Pattern Recogn Lett. 2006;27(8):861–74.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ferri C, Flach PA, Hernandez-Orallo J. Learning decision trees using the area under the roc curve. In: Proceedings of the 19th International Conference on Machine Learning; 2002.Google Scholar
  6. 6.
    Green DM, Swets JA, editors. Swets signal detection theory and psychophysics. New York: Wiley; 1966.Google Scholar
  7. 7.
    Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology. 1982;143(1):29–36.CrossRefGoogle Scholar
  8. 8.
    Joachims T. A support vector method for multivariate performance measures. In: Proceedings of the 22nd International Conference on Machine Learning; 2005.Google Scholar
  9. 9.
    Lusted LB. Signal detectability and medical decision making. Science. 1971;171(3977):1217.CrossRefGoogle Scholar
  10. 10.
    McNeil BJ, Hanley JA. Statistical approaches to the analysis of the receiver operating characteristic (roc) curves. Med Decis Mak. 1984;4(2): 137–50.CrossRefGoogle Scholar
  11. 11.
    Peterson WW, Birdsall TG, Fox WC. The theory of signal detectability. IRE Trans. 1954;PGIT-4(4): 171.Google Scholar
  12. 12.
    Provost FJ, Fawcett T. Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining; 1997. p. 43–8.Google Scholar
  13. 13.
    Sackett DL. Clinical diagnosis and the clinical laboratory. Clin Invest Med. 1978;1:37.Google Scholar
  14. 14.
    Spackman KA. Signal detection theory: valuable tools for evaluating inductive learning. In: Proceedings of the 6th International Workshop on Machine Learning; 1989. p. 160–3.CrossRefGoogle Scholar
  15. 15.
    Swets JA. The relative operating characteristics in psychology. Science. 1973;182(4116):990.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Michigan State UniversityEast LansingUSA

Section editors and affiliations

  • Kyuseok Shim
    • 1
  1. 1.School of Elec. Eng. and Computer ScienceSeoul National Univ.SeoulRepublic of Korea