Novel Feature Selection Method for Nonlinear Support Vector Regression
The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve the nonlinear feature selection problem in the...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2022-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4740173 |
| _version_ | 1811188211886587904 |
|---|---|
| author | Kejia Xu Ying Xu Yafen Ye Weijie Chen |
| author_facet | Kejia Xu Ying Xu Yafen Ye Weijie Chen |
| author_sort | Kejia Xu |
| collection | DOAJ |
| description | The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve the nonlinear feature selection problem in the regression technology field. FS-NSVR preserves the representative features selected in the complex nonlinear system due to its use of a feature selection matrix in the original space. FS-NSVR is a challenging mixed-integer programming problem that is solved efficiently by using an alternate iterative greedy algorithm. Experimental results on three artificial datasets and five real-world datasets confirm that FS-NSVR effectively selects representative features and discards redundant features in a nonlinear system. FS-NSVR outperforms L1-norm support vector regression, L1-norm least squares support vector regression, and Lp-norm support vector regression on both feature selection ability and regression efficiency. |
| first_indexed | 2024-04-11T14:15:50Z |
| format | Article |
| id | doaj.art-6259be7ee2954059bb75111b30c164dd |
| institution | Directory Open Access Journal |
| issn | 1099-0526 |
| language | English |
| last_indexed | 2024-04-11T14:15:50Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj.art-6259be7ee2954059bb75111b30c164dd2022-12-22T04:19:28ZengHindawi-WileyComplexity1099-05262022-01-01202210.1155/2022/4740173Novel Feature Selection Method for Nonlinear Support Vector RegressionKejia Xu0Ying Xu1Yafen Ye2Weijie Chen3School of EconomicsSchool of EconomicsSchool of EconomicsZhijiang CollegeThe development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve the nonlinear feature selection problem in the regression technology field. FS-NSVR preserves the representative features selected in the complex nonlinear system due to its use of a feature selection matrix in the original space. FS-NSVR is a challenging mixed-integer programming problem that is solved efficiently by using an alternate iterative greedy algorithm. Experimental results on three artificial datasets and five real-world datasets confirm that FS-NSVR effectively selects representative features and discards redundant features in a nonlinear system. FS-NSVR outperforms L1-norm support vector regression, L1-norm least squares support vector regression, and Lp-norm support vector regression on both feature selection ability and regression efficiency.http://dx.doi.org/10.1155/2022/4740173 |
| spellingShingle | Kejia Xu Ying Xu Yafen Ye Weijie Chen Novel Feature Selection Method for Nonlinear Support Vector Regression Complexity |
| title | Novel Feature Selection Method for Nonlinear Support Vector Regression |
| title_full | Novel Feature Selection Method for Nonlinear Support Vector Regression |
| title_fullStr | Novel Feature Selection Method for Nonlinear Support Vector Regression |
| title_full_unstemmed | Novel Feature Selection Method for Nonlinear Support Vector Regression |
| title_short | Novel Feature Selection Method for Nonlinear Support Vector Regression |
| title_sort | novel feature selection method for nonlinear support vector regression |
| url | http://dx.doi.org/10.1155/2022/4740173 |
| work_keys_str_mv | AT kejiaxu novelfeatureselectionmethodfornonlinearsupportvectorregression AT yingxu novelfeatureselectionmethodfornonlinearsupportvectorregression AT yafenye novelfeatureselectionmethodfornonlinearsupportvectorregression AT weijiechen novelfeatureselectionmethodfornonlinearsupportvectorregression |