Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables
This research’s primary goal was to evaluate the performance analysis of the recently constructed smoothed location models (SLMs) for discrimination purposes by combining two kinds of multiple correspondence analysis (MCA) to handle high dimensionality problems arising from the binary variables. A p...
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| Format: | Article |
| Language: | English |
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Universiti Kebangsaan Malaysia
2022
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| Online Access: | https://repo.uum.edu.my/id/eprint/30822/1/SM%2051%2012%202022%204153-4160.pdf |
| _version_ | 1803629985621082112 |
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| author | Hamid, Hashibah Okwonu, Friday Zinzendoff Ahad, Nor Aishah Abdul Rahim, Hasliza |
| author_facet | Hamid, Hashibah Okwonu, Friday Zinzendoff Ahad, Nor Aishah Abdul Rahim, Hasliza |
| author_sort | Hamid, Hashibah |
| collection | UUM |
| description | This research’s primary goal was to evaluate the performance analysis of the recently constructed smoothed location models (SLMs) for discrimination purposes by combining two kinds of multiple correspondence analysis (MCA) to handle high dimensionality problems arising from the binary variables. A previous study of SLM, together with MCA as well as principal component analysis (PCA), displayed that the misclassification rate was still very high with respect to a large number of binary variables. Thus, two new SLMs are constructed in this paper to solve this particular problem. The first model results from the combination of SLM with Burt MCA (denoted as SLM+Burt), and the second one is with the joint correspondence analysis (denoted as SLM+JCA). The findings showed that both models performed well for all sample sizes (n) and all binary variables (b) under investigation, except n=60 and b=25 for the SLM+JCA model. Overall, the SLM+JCA model yields a greater performance in contrast to the SLM+Burt model. Moreover, the concept and procedures of the discrimination for the two-group classification conducted in this paper can be extended to multi-class classification as practitioners often deal with many groups and complexities of variables |
| first_indexed | 2024-07-04T06:46:33Z |
| format | Article |
| id | uum-30822 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:46:33Z |
| publishDate | 2022 |
| publisher | Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | uum-308222024-05-29T10:37:10Z https://repo.uum.edu.my/id/eprint/30822/ Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables Hamid, Hashibah Okwonu, Friday Zinzendoff Ahad, Nor Aishah Abdul Rahim, Hasliza QA Mathematics This research’s primary goal was to evaluate the performance analysis of the recently constructed smoothed location models (SLMs) for discrimination purposes by combining two kinds of multiple correspondence analysis (MCA) to handle high dimensionality problems arising from the binary variables. A previous study of SLM, together with MCA as well as principal component analysis (PCA), displayed that the misclassification rate was still very high with respect to a large number of binary variables. Thus, two new SLMs are constructed in this paper to solve this particular problem. The first model results from the combination of SLM with Burt MCA (denoted as SLM+Burt), and the second one is with the joint correspondence analysis (denoted as SLM+JCA). The findings showed that both models performed well for all sample sizes (n) and all binary variables (b) under investigation, except n=60 and b=25 for the SLM+JCA model. Overall, the SLM+JCA model yields a greater performance in contrast to the SLM+Burt model. Moreover, the concept and procedures of the discrimination for the two-group classification conducted in this paper can be extended to multi-class classification as practitioners often deal with many groups and complexities of variables Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/30822/1/SM%2051%2012%202022%204153-4160.pdf Hamid, Hashibah and Okwonu, Friday Zinzendoff and Ahad, Nor Aishah and Abdul Rahim, Hasliza (2022) Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables. Sains Malaysiana, 51 (12). pp. 4153-4160. ISSN 0126-6039 https://journalarticle.ukm.my/21214/ |
| spellingShingle | QA Mathematics Hamid, Hashibah Okwonu, Friday Zinzendoff Ahad, Nor Aishah Abdul Rahim, Hasliza Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables |
| title | Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables |
| title_full | Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables |
| title_fullStr | Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables |
| title_full_unstemmed | Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables |
| title_short | Performance Analysis and Discrimination Procedure of Two-Group Location Model with Some Continuous and High-Dimensional of Binary Variables |
| title_sort | performance analysis and discrimination procedure of two group location model with some continuous and high dimensional of binary variables |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/30822/1/SM%2051%2012%202022%204153-4160.pdf |
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