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Table 7 Classification accuracy with feature/ operational taxonomic unit (OTU) selection, measured by relative classifier information (RCI)

From: A comprehensive evaluation of multicategory classification methods for microbiomic data

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
  1. 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.