Table 4 Intra-dataset classification accuracy of different machine learning methods
Dataset | XGBoost | RF | KNN | SGD | SVM | LR |
|---|---|---|---|---|---|---|
ca1 | 0.937 | 0.885 | 0.828 | 0.797 | 0.895 | 0.836 |
(0.002) | (0.004) | (0.003) | (0.033) | (0.003) | (0.004) | |
ce1 | 0.889 | 0.833 | 0.768 | 0.798 | 0.841 | 0.843 |
(0.014) | (0.019) | (0.019) | (0.045) | (0.015) | (0.014) | |
ce2 | 0.891 | 0.858 | 0.768 | 0.819 | 0.862 | 0.847 |
(0.016) | (0.018) | (0.019) | (0.034) | (0.012) | (0.016) | |
h1 | 0.824 | 0.769 | 0.731 | 0.746 | 0.795 | 0.770 |
(0.007) | (0.008) | (0.007) | (0.011) | (0.007) | (0.007) | |
h2 | 0.904 | 0.869 | 0.857 | 0.860 | 0.879 | 0.892 |
(0.007) | (0.011) | (0.009) | (0.03) | (0.009) | (0.009) | |
h3 | 0.835 | 0.769 | 0.744 | 0.752 | 0.805 | 0.795 |
(0.007) | (0.009) | (0.009) | (0.034) | (0.007) | (0.010) | |
m1 | 0.847 | 0.795 | 0.758 | 0.760 | 0.819 | 0.800 |
(0.015) | (0.016) | (0.022) | (0.038) | (0.019) | (0.019) | |
m2 | 0.900 | 0.826 | 0.797 | 0.798 | 0.873 | 0.833 |
(0.004) | (0.004) | (0.004) | (0.017) | (0.004) | (0.004) |