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A relabeling approach to handling the class imbalance problem for logistic regression
File(s)
Author(s)
Yahze, Li
Adams, Niall
Bellotti, Anthony
Type
Journal Article
Abstract
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.
Date Issued
2022-03-01
Date Acceptance
2021-09-04
Citation
Journal of Computational and Graphical Statistics, 2022, 31, pp.241-253
URI
DOI
ISSN
1061-8600
Publisher
American Statistical Association
Start Page
241
End Page
253
Journal / Book Title
Journal of Computational and Graphical Statistics
Volume
31
Copyright Statement
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which
permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Subjects
Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
EM
High imbalance
Logistic regression
Relabeling
CLASSIFICATION
EXISTENCE
ALGORITHM
Statistics & Probability
0104 Statistics
1403 Econometrics
Publication Status
Published
Date Publish Online
2021-09-10