Repairing concavities in ROC curves

PA Flach, Wu Shaomin

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

58 Citations (Scopus)


In this paper we investigate methods to detect and repair concavities in ROC curves by manipulating model predictions. The basic idea is that, if a point or a set of points lies below the line spanned by two other points in ROC space, we can use this information to repair the concavity. This effectively builds a hybrid model combining the two better models with an inversion of the poorer models; in the case of ranking classifiers, it means that certain intervals of the scores are identified as unreliable and candidates for inversion. We report very encouraging results on 23 UCI data sets, particularly for naive Bayes where the use of two validation folds yielded significant improvements on more than half of them, with only one loss.
Translated title of the contributionRepairing concavities in ROC curves
Original languageEnglish
Title of host publicationUnknown
Pages702 - 707
Number of pages5
ISBN (Print)0938075934
Publication statusPublished - Aug 2005

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05)


Dive into the research topics of 'Repairing concavities in ROC curves'. Together they form a unique fingerprint.

Cite this