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Table 4 Classification accuracy without feature/operational taxonomic unit (OTU) selection, measured by proportion of correct classifications (PCC)

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 0.920 0.911 0.583 0.940 0.598 0.354 0.468 0.695 0.684 0.022
SVM, Linear optimized C 0.920 0.911 0.622 0.980 0.585 0.383 0.485 0.709 0.699 0.038
SVM, Poly 0.920 0.911 0.622 0.980 0.585 0.383 0.484 0.709 0.699 0.036
SVM, RBF 0.909 0.904 0.575 0.973 0.575 0.379 0.451 0.700 0.683 0.021
KRR, Poly 0.913 0.918 0.581 0.954 0.598 0.377 0.482 0.709 0.692 0.027
KRR, RBF 0.923 0.904 0.618 0.967 0.632 0.366 0.467 0.709 0.698 0.030
KNN, K = 1 0.496 0.360 0.195 0.451 0.305 0.249 0.419 0.291 0.346 0.002
KNN, K = 5 0.713 0.339 0.188 0.397 0.281 0.331 0.393 0.300 0.368 0.001
KNN, optimized K 0.714 0.377 0.192 0.325 0.273 0.340 0.409 0.379 0.376 0.001
PNN 0.743 0.321 0.216 0.522 0.332 0.325 0.167 0.247 0.359 0.000
L2-LR, C = 1 0.934 0.939 0.628 0.982 0.628 0.380 0.515 0.725 0.716 0.084*
L2-LR, optimized C 0.933 0.938 0.623 0.978 0.618 0.383 0.502 0.725 0.712 0.067*
L1-LR, C = 1 0.929 0.801 0.559 0.975 0.700 0.422 0.384 0.673 0.680 0.018*
L1-LR, optimized C 0.928 0.903 0.561 0.981 0.690 0.445 0.412 0.692 0.702 0.039
RF, default 0.932 0.955 0.673 0.999 0.744 0.508 0.424 0.730 0.746 0.270*
RF, optimized 0.938 0.956 0.689 0.994 0.760 0.523 0.423 0.735 0.752 -
BLR, Laplace priors 0.927 0.927 0.634 0.962 0.622 0.387 0.452 0.727 0.705 0.042
BLR, Gaussian priors 0.921 0.736 0.480 0.966 0.631 0.354 0.410 0.635 0.642 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.