Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
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Rudin_Margin-based ranking.PDF
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Author(s)
Rudin, Cynthia
Schapire, Robert E.
Date Issued
October 2009
Journal
Journal of Machine Learning Research
Publisher
MIT Press
Citation
Rudin, Cynthia, and Robert E. Schapire. “Margin-based Ranking and an Equivalence
between AdaBoost and RankBoost.” Journal of Machine Learning Research 10 (2009): 2193-2232.
Version
Final published version
Abstract
We study boosting algorithms for learning to rank. We give a general margin-based bound for
ranking based on covering numbers for the hypothesis space. Our bound suggests that algorithms
that maximize the ranking margin will generalize well. We then describe a new algorithm, smooth
margin ranking, that precisely converges to a maximum ranking-margin solution. The algorithm
is a modification of RankBoost, analogous to “approximate coordinate ascent boosting.” Finally,
we prove that AdaBoost and RankBoost are equally good for the problems of bipartite ranking and
classification in terms of their asymptotic behavior on the training set. Under natural conditions,
AdaBoost achieves an area under the ROC curve that is equally as good as RankBoost’s; furthermore,
RankBoost, when given a specific intercept, achieves a misclassification error that is as good
as AdaBoost’s. This may help to explain the empirical observations made by Cortes andMohri, and
Caruana and Niculescu-Mizil, about the excellent performance of AdaBoost as a bipartite ranking
algorithm, as measured by the area under the ROC curve.
Subjects
area under the ROC curve
AdaBoost
generalization bounds
RankBoost
ranking
MIT Department
Sloan School of Management
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DOI of Published Version
http://www.jmlr.org/papers/volume10/rudin09a/rudin09a.pdf
