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Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
Xiaoxuan Zhang · Mingrui Liu · Xun Zhou · Tianbao Yang

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #99
In this paper, we consider online F-measure optimization (OFO). Unlike traditional performance metrics (e.g., classification error rate), F-measure is non-decomposable over training examples and is a non-convex function of model parameters, making it much more difficult to be optimized in an online fashion. Most existing results of OFO usually suffer from high memory/computational costs and/or lack statistical consistency guarantee for optimizing F-measure at the population level. To advance OFO, we propose an efficient online algorithm based on simultaneously learning a posterior probability of class and learning an optimal threshold by minimizing a stochastic strongly convex function with unknown strong convexity parameter. A key component of the proposed method is a novel stochastic algorithm with low memory and computational costs, which can enjoy a convergence rate of $\widetilde O(1/\sqrt{n})$ for learning the optimal threshold under a mild condition on the convergence of the posterior probability, where $n$ is the number of processed examples. It is provably faster than its predecessor based on a heuristic for updating the threshold. The experiments verify the efficiency of the proposed algorithm in comparison with state-of-the-art OFO algorithms.

Author Information

Xiaoxuan Zhang (University of Iowa)
Mingrui Liu (The University of Iowa)
Xun Zhou (University of Iowa)
Tianbao Yang (The University of Iowa)

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