Table 5 Outcome prediction performance of different approaches in simulated scenarios for the test dataset
From: Artificial Intelligence based wrapper for high dimensional feature selection
Methods | Performance (RMSE) | ||||||
|---|---|---|---|---|---|---|---|
Marginal Model Scenarios | Interaction Model Scenarios | ||||||
1_M | 2_M | 3_M | 4_M | 1_I | 2_I | 3_I | |
Mean (95% Confidence Interval) | |||||||
ALASSO | 0.44 (0.35–0.54) | 0.28 (0.23–0.33) | 0.39 (0.32–0.46) | 0.30 (0.26–0.35) | 0.44 (0.36–0.52) | 0.94 (0.74–1.13) | 1.36 (1.31–1.41) |
GLASSO |  |  |  |  | 0.36 (0.3–0.43) | 0.65 (0.51–0.80) | 1.20 (1.15–1.26) |
LASSO | 0.45 (0.36–0.54) | 0.29 (0.24–0.34) | 0.40 (0.33–0.47) | 0.31 (0.26–0.36) | 0.40 (0.33–0.47) | 0.94 (0.76–1.13) | 1.36 (1.32–1.40) |
SPLS | 0.45 (0.35–0.55) | 0.26 (0.21–0.31) | 0.43 (0.28–0.58) | 0.27 (0.23–0.31) | 0.52 (0.38–0.66) | 1.33 (1.21–1.45) | 1.47 (1.38–1.56) |
Enet | 0.45 (0.36–0.53) | 0.29 (0.24–0.35) | 0.42 (0.34–0.5) | 0.32 (0.27–0.36) | 0.41 (0.34–0.49) | 1.02 (0.82–1.22) | 1.34 (1.29–1.38) |
AEnet | 0.46 (0.35–0.57) | 0.28 (0.23–0.33) | 0.41 (0.33–0.48) | 0.31 (0.26–0.35) | 0.46 (0.38–0.54) | 0.97 (0.79–1.15) | 1.34 (1.30–1.39) |
AIWRAP-L | 0.51 (0.38–0.65) | 0.28 (0.23–0.32) | 0.43 (0.34–0.52) | 0.31 (0.26–0.36) | 0.36 (0.29–0.43) | 0.50 (0.40–0.61) | 1.43 (1.30–1.57) |
AIWRAP-LLr | 0.41 (0.26–0.56) | 0.26 (0.21–0.31) | 0.33 (0.27–0.39) | 0.27 (0.22–0.32) | 0.39 (0.31–0.48) | 0.58 (0.39–0.77) | 1.44 (1.33–1.55) |
AIWRAP-LR | 0.46 (0.33–0.58) | 0.30 (0.26–0.33) | 0.34 (0.30–0.38) | 0.29 (0.26–0.33) | 0.56 (0.48–0.65) | 0.79 (0.68–0.91) | 1.35 (1.28–1.41) |