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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2020
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| 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 https://etd.uum.edu.my/8600/ |
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| author | Ahmad Hakiim, Jamaluddin |
| author_facet | Ahmad Hakiim, Jamaluddin |
| author_sort | Ahmad Hakiim, Jamaluddin |
| building | UUM Library |
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| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uum.etd-8600 |
| institution | Universiti Utara Malaysia |
| language | en en en en |
| publishDate | 2020 |
| record_format | eprints |
| spelling | my.uum.etd-86002021-08-29T07:16:20Z https://etd.uum.edu.my/8600/ Classification for large number of variables with two imbalanced groups Ahmad Hakiim, Jamaluddin 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 NonPeerReviewed text en https://etd.uum.edu.my/8600/1/DEPOSIT%20PERMISSION%20NOT%20ALLOW_s822665.pdf text en https://etd.uum.edu.my/8600/2/s822665_01.pdf text en https://etd.uum.edu.my/8600/3/s822665_02.pdf text en https://etd.uum.edu.my/8600/4/s822665_references.docx Ahmad Hakiim, Jamaluddin (2020) Classification for large number of variables with two imbalanced groups. Masters thesis, Universiti Utara Malaysia. |
| spellingShingle | QA Mathematics Ahmad Hakiim, Jamaluddin Classification for large number of variables with two imbalanced groups |
| title | 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_short | Classification for large number of variables with two imbalanced groups |
| title_sort | classification for large number of variables with two imbalanced groups |
| topic | QA Mathematics |
| 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 https://etd.uum.edu.my/8600/ |
| url_provider | http://etd.uum.edu.my/ |