Optimising area under the ROC curve using gradient descent

Alan Herschtal, Bhavani Raskutti

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

81 Citations (Scopus)

Abstract

This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent. The problems with using the AUC statistic as an objective function are that it is non-differentiable, and of complexity O(n2) in the number of data observations. RankOpt uses a differentiable approximation to the AUC which is accurate, and computationally efficient, being of complexity O(n). This enables the gradient descent to be performed in reasonable time. The performance of RankOpt is compared with a number of other linear binary classifiers, over a number of different classification problems. In almost all cases it is found that the performance of RankOpt is significantly better than the other classifiers tested.

Original languageEnglish
Title of host publicationProceedings, Twenty-First International Conference on Machine Learning, ICML 2004
EditorsR. Greiner, D. Schuurmans
Pages385-392
Number of pages8
Publication statusPublished - 2004
Externally publishedYes
EventInternational Conference on Machine Learning 2004 - Banff, Alta, Canada
Duration: 4 Jul 20048 Jul 2004
Conference number: 21st

Conference

ConferenceInternational Conference on Machine Learning 2004
Abbreviated titleICML 2004
CountryCanada
CityBanff, Alta
Period4/07/048/07/04

Cite this