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An experimental comparison of cross-validation techniques for estimating the area under the ROC curve

2010; Elsevier BV; Volume: 55; Issue: 4 Linguagem: Inglês

10.1016/j.csda.2010.11.018

1872-7352

Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski,

Anomaly Detection Techniques and Applications

Reliable estimation of the classification performance of inferred predictive models is difficult when working with small data sets. Cross-validation is in this case a typical strategy for estimating the performance. However, many standard approaches to cross-validation suffer from extensive bias or variance when the area under the ROC curve (AUC) is used as the performance measure. This issue is explored through an extensive simulation study. Leave-pair-out cross-validation is proposed for conditional AUC-estimation, as it is almost unbiased, and its deviation variance is as low as that of the best alternative approaches. When using regularized least-squares based learners, efficient algorithms exist for calculating the leave-pair-out cross-validation estimate.