Table 4 Cross-validation results of the model.
From: Fault diagnosis using ISMA to optimize SVM parameters for aircraft engine damage repair
Data set | Model | Accuracy rate | Precision rate | Recall | F1 score | p-value (vs. SVM) |
|---|---|---|---|---|---|---|
C-MAPSS | SVM | 0.8431 ± 0.032 | 0.7586 ± 0.041 | 0.7197 ± 0.038 | 0.7197 ± 0.035 | / |
WOA-SVM | 0.9159 ± 0.032 | 0.8966 ± 0.041 | 0.888 ± 0.029 | 0.888 ± 0.027 | 0.002 | |
SMA-SVM | 0.9404 ± 0.032 | 0.931 ± 0.021 | 0.9286 ± 0.019 | 0.9286 ± 0.017 | 0.001 | |
ISMA-SVM | 0.9724 ± 0.012 | 0.9655 ± 0.021 | 0.965 ± 0.013 | 0.965 ± 0.011 | 0.0005 | |
Build your own simulation data set | SVM | 0.9151 ± 0.028 | 0.9102 ± 0.030 | 0.9106 ± 0.026 | 0.9106 ± 0.024 | / |
WOA-SVM | 0.9433 ± 0.021 | 0.9391 ± 0.023 | 0.9396 ± 0.020 | 0.9396 ± 0.019 | 0.003 | |
SMA-SVM | 0.9543 ± 0.016 | 0.9519 ± 0.018 | 0.9523 ± 0.015 | 0.9523 ± 0.014 | 0.0015 | |
ISMA-SVM | 0.9679 ± 0.010 | 0.9647 ± 0.012 | 0.9644 ± 0.011 | 0.9644 ± 0.010 | 0.0008 |
