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A relabeling approach to handling the class imbalance problem for logistic regression

Title: A relabeling approach to handling the class imbalance problem for logistic regression
Authors: Yahze, L
Adams, N
Bellotti, A
Item 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.
Issue Date: 1-Mar-2022
Date of Acceptance: 4-Sep-2021
URI: http://hdl.handle.net/10044/1/92164
DOI: 10.1080/10618600.2021.1978470
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.
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
High imbalance
Logistic regression
Statistics & Probability
0104 Statistics
1403 Econometrics
Publication Status: Published
Online Publication Date: 2021-09-10
Appears in Collections:Statistics
Faculty of Natural Sciences