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Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis

The curse of class imbalance affects the performance of many conventional classification algorithms including linear discriminant analysis (LDA). The data pre-processing approach through some resampling methods such as random oversampling (ROS) and random under sampling (RUS) is one of the treatment...

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Main Authors:	Jamaluddin, Ahmad Hakiim, Mahat, Nor Idayu
Format:	Article
Language:	English
Published:	Universiti Utara Malaysia Press 2021
Subjects:	QA Mathematics
Online Access:	https://repo.uum.edu.my/id/eprint/28789/1/JICT%2020%2001%202021%2083-102.pdf https://doi.org/10.32890/jict.20.1.2021.6358 https://repo.uum.edu.my/id/eprint/28789/ https://e-journal.uum.edu.my/index.php/jict/article/view/jict.20.1.2021.6358 https://doi.org/10.32890/jict.20.1.2021.6358
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spelling	my.uum.repo.287892023-06-19T09:55:33Z https://repo.uum.edu.my/id/eprint/28789/ Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis Jamaluddin, Ahmad Hakiim Mahat, Nor Idayu QA Mathematics The curse of class imbalance affects the performance of many conventional classification algorithms including linear discriminant analysis (LDA). The data pre-processing approach through some resampling methods such as random oversampling (ROS) and random under sampling (RUS) is one of the treatments to alleviate such curse. Previous studies have attempted to address the effect of a resampling method on the performance of LDA. However, some studies contradicted with each other based on different performance measures as well as validation strategies. This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 two-group bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. The analysis on the location and dispersion statistics of the performance measures was further enlightened on: (i) the effect of a resampling method on the performance of LDA, and (ii) the enhancement in the learning fairness of LDA on objects regardless of sample size, hence reducing the effect of the curse of class imbalance. Universiti Utara Malaysia Press 2021 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/28789/1/JICT%2020%2001%202021%2083-102.pdf Jamaluddin, Ahmad Hakiim and Mahat, Nor Idayu (2021) Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis. Journal of Information and Communication Technology, 20 (01). pp. 83-102. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/jict.20.1.2021.6358 https://doi.org/10.32890/jict.20.1.2021.6358 https://doi.org/10.32890/jict.20.1.2021.6358
institution	Universiti Utara Malaysia
building	UUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Utara Malaysia
content_source	UUM Institutional Repository
url_provider	http://repo.uum.edu.my/
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Jamaluddin, Ahmad Hakiim Mahat, Nor Idayu Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
description	The curse of class imbalance affects the performance of many conventional classification algorithms including linear discriminant analysis (LDA). The data pre-processing approach through some resampling methods such as random oversampling (ROS) and random under sampling (RUS) is one of the treatments to alleviate such curse. Previous studies have attempted to address the effect of a resampling method on the performance of LDA. However, some studies contradicted with each other based on different performance measures as well as validation strategies. This manuscript attempted to shed more light on the effect of a resampling method (ROS or RUS) on the performance of LDA based on true positive rate and true negative rate through five validation strategies, i.e. leave-one-out cross-validation, k-fold cross-validation, repeated k-fold cross-validation, naive bootstrap, and .632+ bootstrap. 100 two-group bivariate normally distributed simulated and four real data sets with severe class imbalance ratio were utilised. The analysis on the location and dispersion statistics of the performance measures was further enlightened on: (i) the effect of a resampling method on the performance of LDA, and (ii) the enhancement in the learning fairness of LDA on objects regardless of sample size, hence reducing the effect of the curse of class imbalance.
format	Article
author	Jamaluddin, Ahmad Hakiim Mahat, Nor Idayu
author_facet	Jamaluddin, Ahmad Hakiim Mahat, Nor Idayu
author_sort	Jamaluddin, Ahmad Hakiim
title	Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
title_short	Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
title_full	Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
title_fullStr	Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
title_full_unstemmed	Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
title_sort	validation assessments on resampling method in imbalanced binary classification for linear discriminant analysis
publisher	Universiti Utara Malaysia Press
publishDate	2021
url	https://repo.uum.edu.my/id/eprint/28789/1/JICT%2020%2001%202021%2083-102.pdf https://doi.org/10.32890/jict.20.1.2021.6358 https://repo.uum.edu.my/id/eprint/28789/ https://e-journal.uum.edu.my/index.php/jict/article/view/jict.20.1.2021.6358 https://doi.org/10.32890/jict.20.1.2021.6358
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score	13.211869

Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis

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