A Relabeling Approach to Handling the Class Imbalance Problem for Logistic Regression
针对逻辑回归中少数类样本远少于多数类的不平衡问题，提出一种重标记方法，通过EM算法为少数类分配新标签，提升预测性能。
Logistic regression is a standard procedure for real-world classification problems. The challenge of class imbalance arises in two-class classification problems when the minority class is observed much less than the majority class. This characteristic is endemic in many domains. Work by Owen [2007] has shown that cluster structure among the minority class may be a specific problem in highly imbalanced logistic regression. In this paper, we propose a novel relabeling approach to handle the class imbalance problem when using logistic regression, which essentially assigns new labels to the minority class observations. An Expectation-Maximization algorithm is formalized to serve as a tool for efficiently computing this relabeling. Modeling on such relabeled data can lead to improved predictive performance. We demonstrate the effectiveness of this approach with detailed experiments on real data sets.