Conference proceeding
Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), Vol.31
Advances in Neural Information Processing Systems
01/01/2018
Abstract
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 (O) over tilde (1/root 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.
Details
- Title: Subtitle
- Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization
- Creators
- Mingrui Liu - Univ Iowa, Dept Comp Sci, Iowa City, IA 52242 USAXiaoxuan Zhang - Univ Iowa, Dept Comp Sci, Iowa City, IA 52242 USAXun Zhou - University of IowaTianbao Yang - Univ Iowa, Dept Comp Sci, Iowa City, IA 52242 USA
- Contributors
- S Bengio (Editor)H Wallach (Editor)H Larochelle (Editor)K Grauman (Editor)N CesaBianchi (Editor)R Garnett (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), Vol.31
- Publisher
- Neural Information Processing Systems (Nips)
- Series
- Advances in Neural Information Processing Systems
- ISSN
- 1049-5258
- Number of pages
- 11
- Grant note
- IIS-1545995 / National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Business Analytics; Computer Science
- Record Identifier
- 9984380498602771
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