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Bayesian receiver operating characteristic metric for linear classifiers

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
Pages (from-to)52-59
Number of pages8
JournalPattern Recognition Letters
Publication statusPublished - 1 Dec 2019
Publication typeA1 Journal article-refereed


We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training dataset without the need for computationally expensive cross-validation. We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data. These experiments confirm that the closed-form CBAUC is both faster and more accurate than conventional AUC estimators.


  • Bayesian error estimation, Classification, Receiver operating characteristic curve

Publication forum classification

Field of science, Statistics Finland