Research Article
Novel Feature Selection Method for Nonlinear Support Vector Regression
Table 5
Comparison results of FS-NSVR, L1-SVR, Lp-SVR, and L1-LSSVR for real-world datasets.
| Data sets | Regressor | (%) | NMSE | RMSE | | CPU S. |
| Student | FS-NSVR | 6.250 | 0.094 | 0.037 | 0.913 | 0.070 | L1-SVR | 72.917 | 0.062 | 0.030 | 0.956 | 1.118 | Lp-SVR | 62.500 | 0.065 | 0.030 | 0.980 | 0.400 | L1-LSSVR | 52.083 | 0.062 | 0.025 | 0.960 | 0.001 |
| Traffic | FS-NSVR | 18.519 | 0.905 | 0.946 | 0.102 | 2.007 | L1-SVR | 85.185 | 0.963 | 0.976 | 0.252 | 2.259 | Lp-SVR | 88.889 | 1.083 | 1.035 | 0.530 | 0.457 | L1-LSSVR | 44.444 | 0.952 | 1.602 | 0.057 | 0.001 |
| CNN | FS-NSVR | 7.407 | 0.001 | 0.035 | 1.000 | 0.237 | L1-SVR | 86.420 | 0.00 | 0.011 | 1.000 | 1.249 | Lp-SVR | 9.877 | 0.001 | 0.032 | 0.981 | 0.372 | L1-LSSVR | 8.642 | 0.000 | 0.020 | 0.983 | 0.001 |
| Community | FS-NSVR | 21.569 | 0.379 | 0.138 | 0.918 | 1.108 | L1-SVR | 66.667 | 0.331 | 0.129 | 0.656 | 2.047 | Lp-SVR | 37.255 | 0.337 | 0.130 | 0.637 | 1.822 | L1-LSSVR | 74.510 | 0.340 | 0.130 | 0.731 | 0.001 |
| Pakinson | FS-NSVR | 23.077 | 0.862 | 0.446 | 0.415 | 1.176 | L1-SVR | 42.308 | 1.093 | 0.502 | 0.319 | 1.276 | Lp-SVR | 88.462 | 1.229 | 0.533 | 0.430 | 0.570 | L1-LSSVR | 76.923 | 1.021 | 0.372 | 0.197 | 0.001 |
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