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Table 5 Classification accuracy without feature/operational taxonomic unit (OTU) selection, measured by relative classifier information (RCI)

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

Classifier CBH CS CSS FS FSH BP PDX PBS Averages P values
SVM, Linear C = 1 0.769 0.918 0.674 0.882 0.749 0.158 0.228 0.602 0.623 0.165*
SVM, Linear optimized C 0.771 0.915 0.674 0.958 0.751 0.157 0.241 0.607 0.634 0.294*
SVM, Poly 0.771 0.915 0.674 0.958 0.751 0.162 0.241 0.607 0.635 0.299*
SVM, RBF 0.689 0.907 0.631 0.942 0.731 0.156 0.202 0.561 0.602 0.059*
KRR, Poly 0.765 0.927 0.671 0.911 0.758 0.157 0.230 0.612 0.629 0.206*
KRR, RBF 0.774 0.913 0.675 0.935 0.759 0.163 0.242 0.598 0.632 0.265*
KNN, K = 1 0.344 0.329 0.377 0.163 0.355 0.167 0.074 0.078 0.236 0.003
KNN, K = 5 0.178 0.359 0.277 0.102 0.203 0.056 0.092 0.062 0.166 0.002
KNN, optimized K 0.337 0.402 0.354 0.028 0.207 0.089 0.122 0.196 0.217 0.003
PNN 0.325 0.292 0.411 0.236 0.342 0.041 0.070 0.089 0.226 0.002
L2-LR, C = 1 0.772 0.941 0.670 0.964 0.778 0.161 0.236 0.628 0.644 0.575*
L2-LR, optimized C 0.782 0.939 0.680 0.958 0.778 0.163 0.228 0.624 0.644 0.626*
L1-LR, C = 1 0.769 0.825 0.635 0.949 0.779 0.163 0.191 0.565 0.610 0.089*
L1-LR, optimized C 0.798 0.910 0.664 0.960 0.790 0.174 0.209 0.599 0.638 0.439*
RF, default 0.767 0.957 0.671 0.998 0.803 0.173 0.087 0.618 0.634 0.253*
RF, optimized 0.784 0.962 0.681 0.994 0.805 0.225 0.098 0.625 0.647 -
BLR, Laplace priors 0.759 0.932 0.679 0.922 0.770 0.166 0.090 0.619 0.617 0.085*
BLR, Gaussian priors 0.744 0.759 0.496 0.930 0.736 0.077 0.014 0.529 0.536 0.008
  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.