Research Article
Evaluating the Performance of Feature Selection Methods Using Huge Big Data: A Monte Carlo Simulation Approach
Table 3
Variable selection under Autocorrelation from Monte Carlo Simulation.
| | Models | = 0.25, P = 50 | = 0.25, P = 70 | | n = 80/160/320 | Potency | Gauge | Potency | Gauge |
| | MCP | 1/1/1 | 0.04/0.02/0.02 | 1/1/1 | 0.04/0.02/0.02 | | E-SCAD | 1/1/1 | 0.13/0.10/0.10 | 1/1/1 | 0.12/0.09/0.09 | | AEnet | 0.99/1/1 | 0.01/0/0 | 0.99/1/1 | 0.02/0/0 | | Autometrics | 0.99/1/1 | 0.01/0.01/0.01 | 0.99/1/1 | 0.05/0.01/0 | | n = 80/160/320 | = 0.50, P = 50 | = 0.50, P = 70 | | MCP | 0.99/1/1 | 0.06/0.02/0.02 | 0.99/1/1 | 0.08/0.02/0.01 | | E-SCAD | 1/1/1 | 0.15/0.10/0.10 | 0.99/1/1 | 0.14/0.09/0.09 | | AEnet | 0.99/1/1 | 0.02/0/0 | 0.99/1/1 | 0.03/0/0 | | Autometrics | 0.99/1/1 | 0.01/0.01/0.01 | 0.99/1/1 | 0.05/0.01/0.01 | | n = 80/160/320 | = 0.90, P = 50 | = 0.90, P = 70 | | MCP | 0.91/0.99/1 | 0.16/0.12/0.05 | 0.82/0.99/1 | 0.14/0.11/0.05 | | E-SCAD | 0.98/0.99/1 | 0.28/0.23/0.15 | 0.96/0.99/1 | 0.26/0.22/0.13 | | AEnet | 0.94/0.99/0.99 | 0.04/0.01/0 | 0.92/0.99/0.99 | 0.06/0.01/0 | | Autometrics | 0.82/0.98/0.99 | 0.03/0.01/0.01 | 0.76/0.97/0.99 | 0.10/0.01/0.01 |
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