Table 1.
Comparison of feature selection performances across four scenarios.
| Method | TPR | FPR | F1 |
|---|---|---|---|
| Setting1 | |||
| Bi-Bootstrap + Deep IDA | 1.00(0.00) | 0.00(0.00) | 1.00(0.00) |
| TS + Deep CCA | 0.08(0.03) | 0.10(0.01) | 0.08(0.03) |
| Sparse CCA via SELP | 0.87(0.14) | 0.00(0.00) | 0.92(0.11) |
| PMA | 1.00(0.00) | 1.00(0.00) | 0.18(0.00) |
| Setting2 | |||
| Bi-Bootstrap + Deep IDA | 0.64(0.06) | 0.04(0.01) | 0.64(0.06) |
| TS + Deep CCA | 0.10(0.03) | 0.10(0.00) | 0.08(0.03) |
| Sparse CCA via SELP | 0.87(0.13) | 0.00(0.00) | 0.92(0.10) |
| PMA | 1.00(0.00) | 1.00(0.00) | 0.18(0.00) |
| Setting3 | |||
| Bi-Bootstrap + Deep IDA | 0.96(0.02) | 0.00(0.00) | 0.96(0.02) |
| TS + Deep CCA | 0.09 (0.02) | 0.10(0.00) | 0.09(0.02) |
| Sparse CCA via SELP | 0.98(0.00) | 0.00(0.00) | 0.99(0.00) |
| PMA | 0.84(0.29) | 0.73(0.44) | 0.27(0.18) |
| Setting4 | |||
| Bi-Bootstrap + Deep IDA | 0.83(0.04) | 0.02(0.01) | 0.83(0.04) |
| TS + Deep CCA | 0.14(0.06) | 0.10(0.01) | 0.14(0.06) |
| Sparse CCA via SELP | 0.93(0.21) | 0.00(0.00) | 0.94(0.00) |
| PMA | 1.00(0.00) | 1.00(0.00) | 0.18(0.00) |
This table presents the TPR, FPR, and F1 Score for various feature selection methods across four scenarios in nonlinear simulations. Each entry displays the mean values derived from 20 simulations, with the standard deviation provided in parentheses. All values rounded to 2 digits.