Improved nu-support vector regression algorithm based on principal component analysis
Principal component analysis (PCA) is the most commonly used approach for analysing high-dimensional data in order to achieve dimension reduction. However, outliers have an adverse effect on the PCA, and hence reduce the accuracy of the prediction model. To date, no research has been done to incorpo...
| Main Authors: | , |
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| Format: | Article |
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Editura Academia de Studii Economice
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108931/ |
| _version_ | 1848865239935746048 |
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| author | Abdullah Mohammed, Rashid Habshah, Midi |
| author_facet | Abdullah Mohammed, Rashid Habshah, Midi |
| author_sort | Abdullah Mohammed, Rashid |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Principal component analysis (PCA) is the most commonly used approach for analysing high-dimensional data in order to achieve dimension reduction. However, outliers have an adverse effect on the PCA, and hence reduce the accuracy of the prediction model. To date, no research has been done to incorporate the PCA into the algorithm of support vector regression (SVR) technique in order to obtain an accurate prediction model with high accuracy. This paper focuses on improving the nu-SVR algorithm to handle the problem of outliers. A new hybrid PCA with the nu-SVR technique (PCA-SVR) has been established. The performance of the proposed PCA-SVR algorithm is extensively assessed by two real data sets and simulation studies. The outcomes indicate that the PCA-SVR algorithm is more efficient and reliable than the nu-SVR. |
| first_indexed | 2025-11-15T14:01:33Z |
| format | Article |
| id | upm-108931 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:01:33Z |
| publishDate | 2023 |
| publisher | Editura Academia de Studii Economice |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1089312024-05-16T14:02:04Z http://psasir.upm.edu.my/id/eprint/108931/ Improved nu-support vector regression algorithm based on principal component analysis Abdullah Mohammed, Rashid Habshah, Midi Principal component analysis (PCA) is the most commonly used approach for analysing high-dimensional data in order to achieve dimension reduction. However, outliers have an adverse effect on the PCA, and hence reduce the accuracy of the prediction model. To date, no research has been done to incorporate the PCA into the algorithm of support vector regression (SVR) technique in order to obtain an accurate prediction model with high accuracy. This paper focuses on improving the nu-SVR algorithm to handle the problem of outliers. A new hybrid PCA with the nu-SVR technique (PCA-SVR) has been established. The performance of the proposed PCA-SVR algorithm is extensively assessed by two real data sets and simulation studies. The outcomes indicate that the PCA-SVR algorithm is more efficient and reliable than the nu-SVR. Editura Academia de Studii Economice 2023 Article PeerReviewed Abdullah Mohammed, Rashid and Habshah, Midi (2023) Improved nu-support vector regression algorithm based on principal component analysis. Economic Computation and Economic Cybernetics Studies And Research, 57 (2). pp. 41-56. ISSN 0585-7511 https://ecocyb.ase.ro/nr2023_2/03_AbdullahMohammedRashid_HabshahMidi.pdf 10.24818/18423264/57.2.23.03 |
| spellingShingle | Abdullah Mohammed, Rashid Habshah, Midi Improved nu-support vector regression algorithm based on principal component analysis |
| title | Improved nu-support vector regression algorithm based on principal component analysis |
| title_full | Improved nu-support vector regression algorithm based on principal component analysis |
| title_fullStr | Improved nu-support vector regression algorithm based on principal component analysis |
| title_full_unstemmed | Improved nu-support vector regression algorithm based on principal component analysis |
| title_short | Improved nu-support vector regression algorithm based on principal component analysis |
| title_sort | improved nu-support vector regression algorithm based on principal component analysis |
| url | http://psasir.upm.edu.my/id/eprint/108931/ http://psasir.upm.edu.my/id/eprint/108931/ http://psasir.upm.edu.my/id/eprint/108931/ |