IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v38y2023i3d10.1007_s00180-022-01288-3.html
   My bibliography  Save this article

Quicksort leave-pair-out cross-validation for ROC curve analysis

Author

Listed:
  • Riikka Numminen

    (University of Turku)

  • Ileana Montoya Perez

    (University of Turku)

  • Ivan Jambor

    (University of Turku
    Turku University Hospital)

  • Tapio Pahikkala

    (University of Turku)

  • Antti Airola

    (University of Turku)

Abstract

Receiver Operating Characteristic (ROC) curve analysis and area under the ROC curve (AUC) are commonly used performance measures in diagnostic systems. In this work, we assume a setting, where a classifier is inferred from multivariate data to predict the diagnostic outcome for new cases. Cross-validation is a resampling method for estimating the prediction performance of a classifier on data not used for inferring it. Tournament leave-pair-out (TLPO) cross-validation has been shown to be better than other resampling methods at producing a ranking of data that can be used for estimating the ROC curves and areas under them. However, the time complexity of TLPOCV, $$O\left( n^2\right)$$ O n 2 , means that it is impractical in many applications. In this article, a method called quicksort leave-pair-out cross-validation (QLPOCV) is presented in order to decrease the time complexity of obtaining a reliable ranking of data to $$O\left( n\log n\right)$$ O n log n . The proposed method is compared with existing ones in an experimental study, demonstrating that in terms of ROC curves and AUC values QLPOCV produces as accurate performance estimation as TLPOCV, outperforming both k-fold and leave-one-out cross-validation.

Suggested Citation

  • Riikka Numminen & Ileana Montoya Perez & Ivan Jambor & Tapio Pahikkala & Antti Airola, 2023. "Quicksort leave-pair-out cross-validation for ROC curve analysis," Computational Statistics, Springer, vol. 38(3), pages 1579-1595, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-022-01288-3
    DOI: 10.1007/s00180-022-01288-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-022-01288-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-022-01288-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Airola, Antti & Pahikkala, Tapio & Waegeman, Willem & De Baets, Bernard & Salakoski, Tapio, 2011. "An experimental comparison of cross-validation techniques for estimating the area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1828-1844, April.
    2. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.
    2. Campisi, Giovanni & Muzzioli, Silvia & De Baets, Bernard, 2024. "A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices," International Journal of Forecasting, Elsevier, vol. 40(3), pages 869-880.
    3. Zatonatska Tetiana & Dluhopolskyi Oleksandr & Artyukh Tatiana & Tymchenko Kateryna, 2022. "Forecasting the Behavior of Target Segments to Activate Advertising Tools: Case of Mobile Operator Vodafone Ukraine," Economics, Sciendo, vol. 10(1), pages 87-104, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-022-01288-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.