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

DOI10.1080/10618600.2021.1978470OpenAlexW3200319634MaRDI QIDQ5083371

Yazhe Li, Tony Bellotti, Niall M. Adams

Publication date: 22 June 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2021.1978470

zbMATH Keywords

logistic regressionEMrelabelinghigh imbalance

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Default risk prediction and feature extraction using a penalized deep neural network

Uses Software

• AS 136

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Mixtures of distributions: A topological approach
• Some recent research in the analysis of mixture distributions
• From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation
• Calculation of Polychotomous Logistic Regression Parameters Using Individualized Regressions
• On the existence of maximum likelihood estimates in logistic regression models
• The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
• 10.1162/1532443041827943
• Separate sample logistic discrimination
• The Sign of the Logistic Regression Coefficient

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