F-measure Maximizing Logistic Regression
F-measure Maximizing Logistic Regression
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DOI：
10.1080/03610918.2022.2081706
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发表时间：
2019-05
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影响因子：
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通讯作者：
Masaaki Okabe;Jun Tsuchida;Hiroshi Yadohisa
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文献类型：
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作者：
Masaaki Okabe;Jun Tsuchida;Hiroshi Yadohisa
Logistic回归是一种广泛应用于多个领域的方法。当将Logistic回归应用于不平衡数据时，其中多数类主导少数类，所有类标签被估计为“多数类”。在这项研究中，我们使用F-度量优化方法来改进Logistic回归应用于非平衡数据的性能。虽然许多F-测度优化方法采用估计量的比率来逼近F-测度，但估计量的比率往往比直接逼近比率时表现出更大的偏差。因此，我们使用一个近似的F-测量来估计相对密度比。此外，我们还定义并逼近了一个相对F-测度。我们给出了一个关于F-测度的Logistic回归加权逼近的算法。使用真实数据进行的实验结果表明，该算法能够有效地提高Logistic回归处理非平衡数据的性能。
Abstract Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, wherein the majority classes dominate the minority classes, all class labels are estimated as “majority class.” In this study, we use an F-measure optimization method to improve the performance of logistic regression applied to imbalanced data. Although many F-measure optimization methods adopt a ratio of the estimators to approximate the F-measure, the ratio of the estimators tends to exhibit more bias than when the ratio is directly approximated. Therefore, we employ an approximate F-measure to estimate the relative density ratio. In addition, we define and approximate a relative F-measure. We present an algorithm for a logistic regression weighted approximation relative to the F-measure. The results of an experiment using real world data demonstrate that our proposed algorithm can efficiently improve the performance of logistic regression applied to imbalanced data.