F-measure Maximizing Logistic Regression
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866908493560152064 |
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| author | Okabe, Masaaki Tsuchida, Jun Yadohisa, Hiroshi |
| author_facet | Okabe, Masaaki Tsuchida, Jun Yadohisa, Hiroshi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1905_02535 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | F-measure Maximizing Logistic Regression Okabe, Masaaki Tsuchida, Jun Yadohisa, Hiroshi Methodology Machine Learning 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. |
| title | F-measure Maximizing Logistic Regression |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/1905.02535 |