efficient optimization of f-measure with cost-sensitive svm

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ID: 162424
2016
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Abstract
F-measure is one of the most commonly used performance metrics in classification, particularly when the classes are highly imbalanced. Direct optimization of this measure is often challenging, since no closed form solution exists. Current algorithms design the classifiers by using the approximations to the F-measure. These algorithms are not efficient and do not scale well to the large datasets. To fill the gap, in this paper, we propose a novel algorithm, which can efficiently optimize F-measure with cost-sensitive SVM. First of all, we present an explicit transformation from the optimization of F-measure to cost-sensitive SVM. Then we adopt bundle method to solve the inner optimization. For the problem where the existing bundle method may have the fluctuations in the primal objective during iterations, an additional line search procedure is involved, which can alleviate the fluctuations problem and make our algorithm more efficient. Empirical studies on the large-scale datasets demonstrate that our algorithm can provide significant speedups over current state-of-the-art F-measure based learners, while obtaining better (or comparable) precise solutions.
Reference Key
cheng2016mathematicalefficient Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Fan Cheng;Yuan Zhou;Jian Gao;Shuangqiu Zheng
Journal journal of power sources
Year 2016
DOI
10.1155/2016/5873769
URL
http://dx.doi.org/10.1155/2016/5873769
Keywords
civil engineering (general)mathematics engineering (general)

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