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Poster
Stochastic Online AUC Maximization
Yiming Ying · Longyin Wen · Siwei Lyu

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #177 #None
in Wednesday Posters »

Area under ROC (AUC) is a metric which is widely used for measuring the classification performance for imbalanced data. It is of theoretical and practical interest to develop online learning algorithms that maximizes AUC for large-scale data. A specific challenge in developing online AUC maximization algorithm is that the learning objective function is usually defined over a pair of training examples of opposite classes, and existing methods achieves on-line processing with higher space and time complexity. In this work, we propose a new stochastic online algorithm for AUC maximization. In particular, we show that AUC optimization can be equivalently formulated as a convex-concave saddle point problem. From this saddle representation, a stochastic online algorithm (SOLAM) is proposed which has time and space complexity of one datum. We establish theoretical convergence of SOLAM with high probability and demonstrate its effectiveness and efficiency on standard benchmark datasets.

Keywords: Learning Theory · Online Learning · Large Scale Learning and Big Data · Convex Optimization
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Author Information

Yiming Ying (State University of New York at Albany)
Longyin Wen (State University of New York at Albany)
Siwei Lyu (State University of New York at Albany)

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