Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6853114 | Artificial Intelligence | 2016 | 29 Pages |
Abstract
AUC is an important performance measure that has been used in diverse tasks, such as class-imbalanced learning, cost-sensitive learning, learning to rank, etc. In this work, we focus on one-pass AUC optimization that requires going through training data only once without having to store the entire training dataset. Conventional online learning algorithms cannot be applied directly to one-pass AUC optimization because AUC is measured by a sum of losses defined over pairs of instances from different classes. We develop a regression-based algorithm which only needs to maintain the first and second-order statistics of training data in memory, resulting in a storage requirement independent of the number of training data. To efficiently handle high-dimensional data, we develop two deterministic algorithms that approximate the covariance matrices. We verify, both theoretically and empirically, the effectiveness of the proposed algorithms.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Wei Gao, Lu Wang, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou,