Table 7.
Classification accuracy with feature/ operational taxonomic unit (OTU) selection, measured by relative classifier information (RCI)
| Classifier | Best FS Method | CBH | CS | CSS | FS | FSH | BP | PDX | PBS | Averages | P values |
|---|---|---|---|---|---|---|---|---|---|---|---|
|
SVM, Linear C = 1 |
SVM-RFE |
0.719 |
0.952 |
0.691 |
0.929 |
0.813 |
0.334 |
0.337 |
0.674 |
0.681 |
0.191* |
|
SVM, Linear optimized C
|
SVM-RFE
|
0.852
|
0.946 |
0.723
|
0.971 |
0.840
|
0.314 |
0.325 |
0.653 |
0.703 |
- |
|
SVM, Poly |
SVM-RFE |
0.845 |
0.941 |
0.716 |
0.969 |
0.840 |
0.316 |
0.323 |
0.644 |
0.699 |
0.369* |
|
SVM, RBF |
SVM-RFE |
0.813 |
0.925 |
0.683 |
0.972 |
0.813 |
0.286 |
0.290 |
0.611 |
0.674 |
0.089* |
|
KRR, Poly |
SVM-RFE |
0.759 |
0.939 |
0.683 |
0.931 |
0.800 |
0.297 |
0.290 |
0.626 |
0.666 |
0.061* |
|
KRR, RBF |
SVM-RFE |
0.807 |
0.935 |
0.687 |
0.944 |
0.801 |
0.297 |
0.316 |
0.633 |
0.677 |
0.097* |
|
KNN, K = 1 |
RFVS2 |
0.830 |
0.779 |
0.657 |
0.939 |
0.736 |
0.168 |
0.251 |
0.510 |
0.609 |
0.015 |
|
KNN, K = 5 |
RFVS2 |
0.774 |
0.744 |
0.625 |
0.884 |
0.736 |
0.153 |
0.224 |
0.522 |
0.583 |
0.008 |
|
KNN, optimized K |
RFVS2 |
0.829 |
0.773 |
0.652 |
0.914 |
0.736 |
0.179 |
0.221 |
0.531 |
0.604 |
0.014 |
|
PNN |
RFVS2 |
0.726 |
0.798 |
0.629 |
0.907 |
0.730 |
0.167 |
0.227 |
0.516 |
0.587 |
0.012 |
|
L2-LR, C = 1 |
ALL |
0.772 |
0.941 |
0.670 |
0.964 |
0.778 |
0.161 |
0.236 |
0.628 |
0.644 |
0.027 |
|
L2-LR, optimized C |
SVM-RFE |
0.780 |
0.940 |
0.692 |
0.837 |
0.811 |
0.234 |
0.257 |
0.612 |
0.645 |
0.034 |
|
L1-LR, C = 1 |
RFVS1 |
0.742 |
0.836 |
0.642 |
0.934 |
0.771 |
0.183 |
0.213 |
0.584 |
0.613 |
0.011 |
|
L1-LR, optimized C |
RFVS1 |
0.786 |
0.914 |
0.696 |
0.985 |
0.784 |
0.166 |
0.238 |
0.598 |
0.646 |
0.033 |
|
RF, default |
RFVS1 |
0.840 |
0.952 |
0.712 |
0.982 |
0.819 |
0.266 |
0.213 |
0.648 |
0.679 |
0.179* |
|
RF, optimized |
RFVS1 |
0.842 |
0.956 |
0.714 |
0.994 |
0.810 |
0.264 |
0.216 |
0.649 |
0.681 |
0.196* |
|
BLR, Laplace priors |
SVM-RFE |
0.822 |
0.932 |
0.692 |
0.982 |
0.824 |
0.317 |
0.318 |
0.640 |
0.691 |
0.313* |
| BLR, Gaussian priors | RFVS2 | 0.761 | 0.855 | 0.625 | 0.968 | 0.770 | 0.208 | 0.202 | 0.570 | 0.620 | 0.018 |
The nominally best performing classifier on average over all datasets is marked with bold, and P values of methods whose performance cannot be deemed statistically worse than the nominally best performing method are marked with “*”. The accuracy of the nominally best performing method for each dataset is underlined.