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 undersampling (RUS) is one of the treatment...
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| Format: | Article |
| Language: | English |
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UUM Press
2020-11-01
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| Series: | Journal of ICT |
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| Online Access: | https://e-journal.uum.edu.my/index.php/jict/article/view/12401 |
| _version_ | 1811314712007147520 |
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| author | Ahmad Hakiim Jamaluddin Nor Idayu Mahat |
| author_facet | Ahmad Hakiim Jamaluddin Nor Idayu Mahat |
| author_sort | Ahmad Hakiim Jamaluddin |
| collection | DOAJ |
| 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 undersampling (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.
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| first_indexed | 2024-04-13T11:17:25Z |
| format | Article |
| id | doaj.art-29bda896486c453d826fd24f222001ab |
| institution | Directory Open Access Journal |
| issn | 1675-414X 2180-3862 |
| language | English |
| last_indexed | 2024-04-13T11:17:25Z |
| publishDate | 2020-11-01 |
| publisher | UUM Press |
| record_format | Article |
| series | Journal of ICT |
| spelling | doaj.art-29bda896486c453d826fd24f222001ab2022-12-22T02:48:55ZengUUM PressJournal of ICT1675-414X2180-38622020-11-01201VALIDATION ASSESSMENTS ON RESAMPLING METHOD IN IMBALANCED BINARY CLASSIFICATION FOR LINEAR DISCRIMINANT ANALYSISAhmad Hakiim Jamaluddin0Nor Idayu Mahat1Department of Mathematics, Universiti Putra Malaysia, MalaysiaCentre for Testing, Measurement and Appraisal, Universiti Utara Malaysia, Malaysia 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 undersampling (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. https://e-journal.uum.edu.my/index.php/jict/article/view/12401Linear discriminant analysispre-processingresampling methodclass imbalancebinary classification |
| spellingShingle | Ahmad Hakiim Jamaluddin Nor Idayu Mahat VALIDATION ASSESSMENTS ON RESAMPLING METHOD IN IMBALANCED BINARY CLASSIFICATION FOR LINEAR DISCRIMINANT ANALYSIS Journal of ICT Linear discriminant analysis pre-processing resampling method class imbalance binary classification |
| title | 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_short | 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 |
| topic | Linear discriminant analysis pre-processing resampling method class imbalance binary classification |
| url | https://e-journal.uum.edu.my/index.php/jict/article/view/12401 |
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