基于逻辑回归的不平衡数据算法适用性研究

Research on the Applicability of Unbalanced Data Algorithm Based on Logistic Regression

逻辑回归模型容易受到不平衡数据的影响,本文主要探究了随机欠采样法、Border Line-Smote (BLS)过采样法、自适应综合过采样法(Synthetic Minority Oversampling Technique)等三种不平衡数据算法对逻辑回归模型的适用情况。利用逻辑回归模型分别对三种方法平衡之后的数据,处理之后发现BLS过采样法得出的各项指标最优,ADASYN过采样法得出的各项指标最差,最终得出BLS过采样法更适用于逻辑回归模型的不平衡数据集的处理。

The logistic regression model is susceptible to the impact of unbalanced data. This paper mainly explores the applicability of three kinds of unbalanced data algorithms, including stochastic under-sampling, Border Line-Smote oversampling (BLS) method, and Synthetic Minority Over-sampling Technique, to the logistic regression model. By using logistic regression model to process the balanced data of the three methods, it was found that the indicators obtained by BLS over-sampling method were the best and the indicators obtained by ADASYN over-sampling method were the worst. Finally, it was concluded that BLS oversampling method was more suitable for the processing of unbalanced data sets of logistic regression model.