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- W4309285812 abstract "Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logistic regression applied to imbalanced data. While many F-measure optimization methods adopt a ratio of the estimators to approximate the F-measure, the ratio of the estimators tends to have more bias than when the ratio is directly approximated. Therefore, we employ an approximate F-measure for estimating the relative density ratio. In addition, we define a relative F-measure and approximate the relative F-measure. We show an algorithm for a logistic regression weighted approximated relative to the F-measure. The experimental results using real world data demonstrated that our proposed method is an efficient algorithm to improve the performance of logistic regression applied to imbalanced data." @default.
- W4309285812 created "2022-11-25" @default.
- W4309285812 creator A5031160077 @default.
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- W4309285812 date "2019-05-07" @default.
- W4309285812 modified "2023-09-26" @default.
- W4309285812 title "F-measure Maximizing Logistic Regression" @default.
- W4309285812 doi "https://doi.org/10.48550/arxiv.1905.02535" @default.
- W4309285812 hasPublicationYear "2019" @default.
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