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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2019
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Materias: | |
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 |
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author | Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon |
author_facet | Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon |
author_sort | Zhang, Lili |
collection | PubMed |
description | 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 the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions. |
format | Online Article Text |
id | pubmed-9041569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-90415692022-06-14 A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon J Appl Stat Application Notes 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 the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions. Taylor & Francis 2019-07-23 /pmc/articles/PMC9041569/ /pubmed/35706966 http://dx.doi.org/10.1080/02664763.2019.1643829 Text en © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by-nc-nd/4.0/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/ (https://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. |
spellingShingle | Application Notes Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
title | A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
title_full | A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
title_fullStr | A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
title_full_unstemmed | A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
title_short | A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
title_sort | descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data |
topic | Application Notes |
url | 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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