Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods
This paper aimed to determine the efficiency of classifiers for high-dimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed a...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2022
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| Online Access: | https://repo.uum.edu.my/id/eprint/28745/1/JICT%2021%2003%202022%20437-464.pdf https://doi.org/10.32890/jict2022.21.3.6 |
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| author | Okwonu, Friday Zinzendoff Ahad, Nor Aishah Ogini, Nicholas Oluwole Okoloko, Innocent Ejiro Wan Husin, Wan Zakiyatussariroh |
| author_facet | Okwonu, Friday Zinzendoff Ahad, Nor Aishah Ogini, Nicholas Oluwole Okoloko, Innocent Ejiro Wan Husin, Wan Zakiyatussariroh |
| author_sort | Okwonu, Friday Zinzendoff |
| collection | UUM |
| description | This paper aimed to determine the efficiency of classifiers for high-dimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed as a metric to analyze the comparative performance for high-dimensional classification methods. A simplified performance metric was derived to show the efficiency of different classification methods. To achieve the objectives, the existing probability of correct classification (PCC) or classification accuracy reported in five different articles was used to generate the BETH value. Then, a comparative analysis was performed between the application of BETH value and the well-established PCC value ,derived from the confusion matrix. The analysis indicated that the BETH procedure had a minimum misclassification rate, unlike the Optimal method. The results also revealed that as the PCC inclined toward unity value, the misclassification rate between the two methods (BETH and PCC) became extremely irrelevant. The study revealed that the BETH method was invariant to the performance established by the classifiers using the PCC criterion but demonstrated more relevant aspects of robustness and minimum misclassification rate as compared to the PCC method. In addition, the comparative analysis affirmed that the BETH method exhibited more robust efficiency than the Optimal method. The study concluded that a minimum misclassification rate yields robust performance efficiency. |
| first_indexed | 2024-07-04T06:39:03Z |
| format | Article |
| id | uum-28745 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:39:03Z |
| publishDate | 2022 |
| publisher | Universiti Utara Malaysia Press |
| record_format | dspace |
| spelling | uum-287452023-03-16T08:28:08Z https://repo.uum.edu.my/id/eprint/28745/ Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods Okwonu, Friday Zinzendoff Ahad, Nor Aishah Ogini, Nicholas Oluwole Okoloko, Innocent Ejiro Wan Husin, Wan Zakiyatussariroh QA Mathematics This paper aimed to determine the efficiency of classifiers for high-dimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed as a metric to analyze the comparative performance for high-dimensional classification methods. A simplified performance metric was derived to show the efficiency of different classification methods. To achieve the objectives, the existing probability of correct classification (PCC) or classification accuracy reported in five different articles was used to generate the BETH value. Then, a comparative analysis was performed between the application of BETH value and the well-established PCC value ,derived from the confusion matrix. The analysis indicated that the BETH procedure had a minimum misclassification rate, unlike the Optimal method. The results also revealed that as the PCC inclined toward unity value, the misclassification rate between the two methods (BETH and PCC) became extremely irrelevant. The study revealed that the BETH method was invariant to the performance established by the classifiers using the PCC criterion but demonstrated more relevant aspects of robustness and minimum misclassification rate as compared to the PCC method. In addition, the comparative analysis affirmed that the BETH method exhibited more robust efficiency than the Optimal method. The study concluded that a minimum misclassification rate yields robust performance efficiency. Universiti Utara Malaysia Press 2022 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/28745/1/JICT%2021%2003%202022%20437-464.pdf Okwonu, Friday Zinzendoff and Ahad, Nor Aishah and Ogini, Nicholas Oluwole and Okoloko, Innocent Ejiro and Wan Husin, Wan Zakiyatussariroh (2022) Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods. Journal of Information and Communication Technology, 21 (03). pp. 437-464. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/14789 https://doi.org/10.32890/jict2022.21.3.6 https://doi.org/10.32890/jict2022.21.3.6 |
| spellingShingle | QA Mathematics Okwonu, Friday Zinzendoff Ahad, Nor Aishah Ogini, Nicholas Oluwole Okoloko, Innocent Ejiro Wan Husin, Wan Zakiyatussariroh Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods |
| title | Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods |
| title_full | Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods |
| title_fullStr | Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods |
| title_full_unstemmed | Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods |
| title_short | Comparative Performance Evaluation of Efficiency for High Dimensional Classification Methods |
| title_sort | comparative performance evaluation of efficiency for high dimensional classification methods |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/28745/1/JICT%2021%2003%202022%20437-464.pdf https://doi.org/10.32890/jict2022.21.3.6 |
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