A1 Journal article – refereed
An experimental comparison of cross-validation techniques for estimating the area under the ROC curve




List of Authors: Airola A, Pahikkala T, Waegeman W, De Baets B, Salakoski T
Publisher: ELSEVIER SCIENCE BV
Publication year: 2011
Journal: Computational Statistics and Data Analysis
Journal name in source: COMPUTATIONAL STATISTICS & DATA ANALYSIS
Journal acronym: COMPUT STAT DATA AN
Number in series: 4
Volume number: 55
Issue number: 4
Number of pages: 17
ISSN: 0167-9473

Abstract
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.

Last updated on 2019-20-07 at 04:31