A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of...

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Detalles Bibliográficos
Autores principales: Zhang, Lili, Ray, Herman, Priestley, Jennifer, Tan, Soon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2019
Materias:
Application Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041569/
https://www.ncbi.nlm.nih.gov/pubmed/35706966
http://dx.doi.org/10.1080/02664763.2019.1643829

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9041569/
https://www.ncbi.nlm.nih.gov/pubmed/35706966
http://dx.doi.org/10.1080/02664763.2019.1643829

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