Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2011
Abstract
Most studies of online learning measure the performance of a learner by classification accuracy, which is inappropriate for applications where the data are unevenly distributed among different classes. We address this limitation by developing online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions. The key challenge of online AUC maximization is that it needs to optimize the pairwise loss between two instances from different classes. This is in contrast to the classical setup of online learning where the overall loss is a sum of losses over individual training examples. We address this challenge by exploiting the reservoir sampling technique, and present two algorithms for online AUC maximization with theoretic performance guarantee. Extensive experimental studies confirm the effectiveness and the efficiency of the proposed algorithms for maximizing AUC.
Discipline
Computer Sciences | Databases and Information Systems | Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
Proceedings of the 28th International Conference on Machine Learning ICML 2011: Bellevue, WA, June 28 - July 2
First Page
233
Last Page
240
ISBN
9781450306195
Publisher
International Machine Learning Society
City or Country
Madison, WI
Citation
ZHAO, Peilin; HOI, Steven C. H.; JIN, Rong; and YANG, Tianbo.
Online AUC maximization. (2011). Proceedings of the 28th International Conference on Machine Learning ICML 2011: Bellevue, WA, June 28 - July 2. 233-240.
Available at: https://ink.library.smu.edu.sg/sis_research/2351
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://www.icml-2011.org/papers/198_icmlpaper.pdf