تقرير
F-measure Maximizing Logistic Regression
| العنوان: | F-measure Maximizing Logistic Regression |
|---|---|
| المؤلفون: | Okabe, Masaaki, Tsuchida, Jun, Yadohisa, Hiroshi |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/1905.02535Test |
| رقم الانضمام: | edsarx.1905.02535 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |