Research Article
Evaluating the Performance of Feature Selection Methods Using Huge Big Data: A Monte Carlo Simulation Approach
Table 2
Variable selection under heteroscedasticity from Monte Carlo Simulation.
| | Models | = 0.1/0.3, P = 50 | = 0.1/0.3, P = 70 |
| | n = 80/160/320 | Potency | Gauge | Potency | Gauge | | MCP | 1/1/1 | 0.08/0.02/0.01 | 1/1/1 | 0.01/0.01/0.01 | | E-SCAD | 1/1/1 | 0.10/0.11/0.11 | 1/1/1 | 0.09/0.10/0.10 | | AEnet | 1/1/1 | 0/0/0 | 1/1/1 | 0/0/0 | | Autometrics | 1/1/1 | 0.01/0.01/0.01 | 1/1/1 | 0.04/0.01/0.01 | | n = 80/160/320 | = 0.2/0.6, P = 50 | = 0.2/0.6, P = 70 | | MCP | 1/1/1 | 0.02/0.01/0.02 | 1/1/1 | 0.01/0.01/0.01 | | E-SCAD | 1/1/1 | 0.10/0.10/0.12 | 1/1/1 | 0.09/0.10/0.10 | | AEnet | 1/1/1 | 0/0/0 | 1/1/1 | 0/0/0 | | Autometrics | 1/1/1 | 0.01/0.01/0.01 | 1/1/1 | 0.04/0.01/0.01 | | n = 80/160/320 | = 0.3/0.9, P = 50 | = 0.3/0.9, P = 70 | | MCP | 1/1/1 | 0.02/0.01/0.02 | 1/1/1 | 0.01/0.01/0.01 | | E-SCAD | 1/1/1 | 0.10/0.10/0.10 | 1/1/1 | 0.09/0.10/0.10 | | AEnet | 1/1/1 | 0/0/0 | 1/1/1 | 0/0/0 | | Autometrics | 1/1/1 | 0.01/0.01/0.01 | 0.99/1/1 | 0.04/0.01/0.01 |
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