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: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia Press
2021
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| Subjects: | |
| 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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| Summary: | 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. |
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