Making classifier performance comparisons when ROC curves intersect

Chiara Gigliarano, Silvia Figini and Pietro Muliere

Computational Statistics & Data Analysis, 2014, vol. 77, issue C, 300-312

Abstract: The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show intersections is proposed. In particular, the relationship between ROC orderings and stochastic dominance is investigated in a theoretical framework and a general class of indicators is proposed which is coherent with dominance criteria also when ROC curves cross. Furthermore, a simulation study and a real application to credit risk data are proposed to illustrate the use of the new methodological approach.

Keywords: ROC curve; AUC measure; Stochastic dominance; Classification; Model selection (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:77:y:2014:i:c:p:300-312

DOI: 10.1016/j.csda.2014.03.008

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