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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
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    References listed on IDEAS

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    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.
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