Table 2.
Comparison of the performance of RF, SVM and ANN under all encoding procedures with both balanced and imbalanced training dataset
| EP | MLA | Balanced Dataset | Imbalanced Dataset | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| TPR | TNR | F (α = 1) | F (β = 2) | G-mean | WA | MCC | TPR | TNR | F (α = 1) | F (β = 2) | G-mean | WA | MCC | ||
| P-1 | RF | 0.954 | 0.924 | 0.940 | 0.932 | 0.939 | 0.939 | 0.878 | 0.842 | 0.896 | 0.865 | 0.880 | 0.869 | 0.869 | 0.739 |
| (0.014) | (0.014) | (0.010) | (0.012) | (0.010) | (0.010) | (0.020) | (0.064) | (0.018) | (0.032) | (0.049) | (0.030) | (0.028) | (0.043) | ||
| SVM | 0.935 | 0.930 | 0.933 | 0.931 | 0.933 | 0.933 | 0.865 | 0.104 | 0.982 | 0.185 | 0.349 | 0.320 | 0.543 | 0.180 | |
| (0.015) | (0.017) | (0.015) | (0.015) | (0.016) | (0.016) | (0.031) | (0.027) | (0.018) | (0.041) | (0.031) | (0.040) | (0.013) | (0.061) | ||
| ANN | 0.892 | 0.896 | 0.894 | 0.895 | 0.894 | 0.894 | 0.787 | 0.032 | 0.988 | 0.061 | 0.136 | 0.178 | 0.510 | 0.068 | |
| (0.064) | (0.080) | (0.063) | (0.062) | (0.066) | (0.065) | (0.129) | (0.026) | (0.010) | (0.046) | (0.032) | (0.065) | (0.011) | (0.055) | ||
| P-2 | RF | 0.937 | 0.901 | 0.920 | 0.911 | 0.919 | 0.919 | 0.838 | 0.883 | 0.894 | 0.888 | 0.891 | 0.888 | 0.889 | 0.777 |
| (0.020) | (0.016) | (0.016) | (0.018) | (0.016) | (0.016) | (0.033) | (0.038) | () | (0.025) | (0.030) | (0.019) | (0.019) | (0.035) | ||
| SVM | 0.720 | 0.773 | 0.740 | 0.752 | 0.746 | 0.746 | 0.493 | 0.321 | 0.989 | 0.482 | 0.689 | 0.563 | 0.655 | 0.417 | |
| (0.029) | (0.106) | (0.041) | (0.026) | (0.049) | (0.051) | (0.108) | (0.051) | (0.008) | (0.055) | (0.053) | (0.043) | (0.025) | (0.048) | ||
| ANN | 0.775 | 0.777 | 0.776 | 0.776 | 0.776 | 0.776 | 0.552 | 0.305 | 0.978 | 0.460 | 0.661 | 0.546 | 0.642 | 0.383 | |
| (0.067) | (0.037) | (0.049) | (0.059) | (0.048) | (0.045) | (0.090) | (0.049) | (0.014) | (0.052) | (0.051) | (0.043) | (0.022) | (0.046) | ||
| P-3 | RF | 0.940 | 0.908 | 0.925 | 0.917 | 0.924 | 0.924 | 0.848 | 0.879 | 0.891 | 0.884 | 0.888 | 0.885 | 0.885 | 0.770 |
| (0.017) | (0.015) | (0.012) | (0.014) | (0.012) | (0.012) | (0.246) | (0.044) | (0.022) | (0.029) | (0.034) | (0.022) | (0.022) | (0.042) | ||
| SVM | 0.789 | 0.807 | 0.796 | 0.800 | 0.798 | 0.798 | 0.595 | 0.249 | 0.988 | 0.395 | 0.609 | 0.496 | 0.619 | 0.352 | |
| (0.044) | (0.068) | (0.042) | (0.042) | (0.046) | (0.045) | (0.090) | (0.052) | (0.008) | (0.062) | (0.056) | (0.049) | (0.026) | (0.055) | ||
| ANN | 0.757 | 0.760 | 0.758 | 0.759 | 0.759 | 0.759 | 0.517 | 0.272 | 0.979 | 0.421 | 0.626 | 0.516 | 0.626 | 0.355 | |
| (0.118) | (0.099) | (0.067) | (0.098) | (0.057) | (0.048) | (0.086) | (0.066) | (0.009) | (0.081) | (0.072) | (0.064) | (0.034) | (0.076) | ||
The values inside the brackets () are the standard errors
EP encoding procedure, MLA machine learning approaches