ROBUST VARIABLE SELECTION FOR SINGLE INDEX SUPPORT VECTOR REGRESSION MODEL
The single index support vector regression model (SI-SVR) is a useful regression technique used to alleviate the problem of high-dimensionality. In this paper, we propose a robust variable selection technique for the SI-SVR model by using vital method to identify and minimize the effects of outliers...
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| Format: | Article |
| Language: | English |
| Published: |
Mustansiriyah University
2019-08-01
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| Series: | Al-Mustansiriyah Journal of Science |
| Subjects: | |
| Online Access: | http://mjs.uomustansiriyah.edu.iq/ojs1/index.php/MJS/article/view/388 |
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| Summary: | The single index support vector regression model (SI-SVR) is a useful regression technique used to alleviate the problem of high-dimensionality. In this paper, we propose a robust variable selection technique for the SI-SVR model by using vital method to identify and minimize the effects of outliers in the data set. The effectiveness of the proposed robust variable selection of the SI-SVR model is explored by using various simulation examples. Furthermore, the suggested method is tested by analyzing a real data set which highlights the utility of the proposed methodology. |
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| ISSN: | 1814-635X 2521-3520 |