A Relabeling Approach to Handling the Class Imbalance Problem for Logistic Regression

Yazhe Li, Niall Adams, Tony Bellotti

Research output: Journal PublicationArticlepeer-review

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 has shown that cluster structure among the minority class may be a specific problem in highly imbalanced logistic regression. In this article, 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 datasets. Supplemental materials for the article are available online.

Original languageEnglish
Pages (from-to)241-253
Number of pages13
JournalJournal of Computational and Graphical Statistics
Volume31
Issue number1
Early online date9 Nov 2021
DOIs
Publication statusPublished Online - 9 Nov 2021

Keywords

  • EM
  • High imbalance
  • Logistic regression
  • Relabeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics

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