On equivalence relationships between classification and ranking algorithms
Name
Rudin_On equivalence.pdf
Size
368.15 KB
Format
Adobe PDF
Checksum (MD5)
eeb6b21123cf5953de2672469f608532
Author(s)
Ertekin, Seyda
Rudin, Cynthia
Date Issued
October 2011
Journal
Journal of Machine Learning Research
Publisher
Association for Computing Machinery
Citation
Ertekin, Seyda and Cynthia Rudin. "On Equivalence Relationships Between Classification and Ranking Algorithms." Journal of Machine Learning Research 12 (2011) 2905-2929.
Version
Final published version
Abstract
We demonstrate that there are machine learning algorithms that can achieve success for two separate
tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that
advantages gained from solving one task can be carried over to the other task, such as the ability
to obtain conditional density estimates, and an order-of-magnitude reduction in computational
time for training the algorithm. It also means that some algorithms are robust to the choice of
evaluation metric used; they can theoretically perform well when performance is measured either
by a misclassification error or by a statistic of the ROC curve (such as the area under the curve).
Specifically, we provide such an equivalence relationship between a generalization of Freund et
al.’s RankBoost algorithm, called the “P-Norm Push,” and a particular cost-sensitive classification
algorithm that generalizes AdaBoost, which we call “P-Classification.”We discuss and validate the
potential benefits of this equivalence relationship, and perform controlled experiments to understand P-Classification’s empirical performance. There is no established equivalence relationship for logistic regression and its ranking counterpart, so we introduce a logistic-regression-style algorithm that aims in between classification and ranking, and has promising experimental performance with respect to both tasks.
MIT Department
Sloan School of Management
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://jmlr.csail.mit.edu/papers/volume12/ertekin11a/ertekin11a.pdf
