Classification for large number of variables with two imbalanced groups

In the presence of group imbalance and large number of variables problems, traditional classification algorithms tend to be biased towards the majority group. Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance...

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Main Author:	Ahmad Hakiim, Jamaluddin
Format:	Thesis
Language:	eng eng eng eng
Published:	2020
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/8600/1/DEPOSIT%20PERMISSION%20NOT%20ALLOW_s822665.pdf https://etd.uum.edu.my/8600/2/s822665_01.pdf https://etd.uum.edu.my/8600/3/s822665_02.pdf https://etd.uum.edu.my/8600/4/s822665_references.docx
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id	my-uum-etd.8600
record_format	uketd_dc
spelling	my-uum-etd.86002021-08-29T07:16:20Z Classification for large number of variables with two imbalanced groups 2020 Ahmad Hakiim, Jamaluddin Mahat, Nor Idayu Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA Mathematics In the presence of group imbalance and large number of variables problems, traditional classification algorithms tend to be biased towards the majority group. Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance rather than the combination of both problems. This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. The difference between the two algorithms is in terms of the order of resampling and variable extraction prior to the construction of linear discriminant analysis (LDA). Both simulated and real data sets were utilised to measure the performance of the proposed algorithms based on two evaluation indicators, sensitivity and specificity. Based on the findings, Algorithm 2 outperforms Algorithm 1 in classifying the minority group, while both proposed algorithms perform equally well in classifying the majority group. Both proposed algorithms outperform the conventional LDA on principal components (PCA-LDA) in classifying the minority group. Also, this study has proven that the conventional PCA-LDA and conventional LDA are biased towards the majority group. Hence, both algorithms are suggested to be the alternatives for imbalanced classification with large number of variables. Both algorithms are beneficial towards the practitioners of classification predictive modelling as well as statisticians in pattern recognition domain. 2020 Thesis https://etd.uum.edu.my/8600/ https://etd.uum.edu.my/8600/1/DEPOSIT%20PERMISSION%20NOT%20ALLOW_s822665.pdf text eng staffonly https://etd.uum.edu.my/8600/2/s822665_01.pdf text eng staffonly https://etd.uum.edu.my/8600/3/s822665_02.pdf text eng staffonly https://etd.uum.edu.my/8600/4/s822665_references.docx text eng public other masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng eng
advisor	Mahat, Nor Idayu
topic	QA Mathematics
spellingShingle	QA Mathematics Ahmad Hakiim, Jamaluddin Classification for large number of variables with two imbalanced groups
description	In the presence of group imbalance and large number of variables problems, traditional classification algorithms tend to be biased towards the majority group. Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance rather than the combination of both problems. This study proposed two algorithms of classification namely Algorithm 1 and Algorithm 2 which combine resampling, variable extraction, and classification procedure. The difference between the two algorithms is in terms of the order of resampling and variable extraction prior to the construction of linear discriminant analysis (LDA). Both simulated and real data sets were utilised to measure the performance of the proposed algorithms based on two evaluation indicators, sensitivity and specificity. Based on the findings, Algorithm 2 outperforms Algorithm 1 in classifying the minority group, while both proposed algorithms perform equally well in classifying the majority group. Both proposed algorithms outperform the conventional LDA on principal components (PCA-LDA) in classifying the minority group. Also, this study has proven that the conventional PCA-LDA and conventional LDA are biased towards the majority group. Hence, both algorithms are suggested to be the alternatives for imbalanced classification with large number of variables. Both algorithms are beneficial towards the practitioners of classification predictive modelling as well as statisticians in pattern recognition domain.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Ahmad Hakiim, Jamaluddin
author_facet	Ahmad Hakiim, Jamaluddin
author_sort	Ahmad Hakiim, Jamaluddin
title	Classification for large number of variables with two imbalanced groups
title_short	Classification for large number of variables with two imbalanced groups
title_full	Classification for large number of variables with two imbalanced groups
title_fullStr	Classification for large number of variables with two imbalanced groups
title_full_unstemmed	Classification for large number of variables with two imbalanced groups
title_sort	classification for large number of variables with two imbalanced groups
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2020
url	https://etd.uum.edu.my/8600/1/DEPOSIT%20PERMISSION%20NOT%20ALLOW_s822665.pdf https://etd.uum.edu.my/8600/2/s822665_01.pdf https://etd.uum.edu.my/8600/3/s822665_02.pdf https://etd.uum.edu.my/8600/4/s822665_references.docx
_version_	1747828421066489856

Classification for large number of variables with two imbalanced groups

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