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- W2384341180 abstract "<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-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<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M3><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-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<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M4><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-measure with cost-sensitive SVM. First of all, we present an explicit transformation from the optimization of<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M5><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-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<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M6><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-measure based learners, while obtaining better (or comparable) precise solutions." @default.
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- W2384341180 date "2016-01-01" @default.
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- W2384341180 title "Efficient Optimization of<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-Measure with Cost-Sensitive SVM" @default.
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- W2384341180 doi "https://doi.org/10.1155/2016/5873769" @default.
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