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Table 6 Classification accuracy with 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 Best FS Method CBH CS CSS FS FSH BP PDX PBS Averages P values
SVM, Linear C = 1 SVM-RFE 0.900 0.941 0.610 0.965 0.719 0.524 0.558 0.759 0.747 0.319*
SVM, Linear optimized C SVM-RFE 0.952 0.935 0.631 0.985 0.754 0.534 0.553 0.761 0.763 0.535*
SVM, Poly SVM-RFE 0.950 0.929 0.633 0.987 0.742 0.528 0.551 0.754 0.759 0.460*
SVM, RBF SVM-RFE 0.941 0.918 0.617 0.987 0.693 0.518 0.523 0.727 0.741 0.179*
KRR, Poly KW 0.909 0.933 0.623 0.949 0.749 0.547 0.514 0.713 0.742 0.199*
KRR, RBF KW 0.929 0.939 0.634 0.970 0.737 0.537 0.504 0.714 0.745 0.248*
KNN, K = 1 RFVS2 0.930 0.760 0.563 0.971 0.623 0.421 0.443 0.596 0.663 0.011
KNN, K = 5 RFVS2 0.930 0.724 0.529 0.943 0.656 0.434 0.434 0.609 0.657 0.009
KNN, optimized K RFVS2 0.935 0.754 0.552 0.963 0.648 0.422 0.432 0.620 0.666 0.011
PNN RFVS2 0.906 0.781 0.560 0.956 0.623 0.130 0.449 0.604 0.626 0.006
L2-LR, C = 1 ALL 0.934 0.939 0.628 0.982 0.628 0.380 0.515 0.725 0.716 0.047
L2-LR, optimized C KW 0.921 0.948 0.650 0.836 0.739 0.499 0.464 0.711 0.721 0.089*
L1-LR, C = 1 RFVS1 0.922 0.818 0.589 0.968 0.706 0.449 0.395 0.687 0.692 0.020
L1-LR, optimized C RFVS1 0.934 0.909 0.611 0.993 0.710 0.442 0.418 0.697 0.714 0.048
RF, default RFVS1 0.954 0.950 0.704 0.991 0.745 0.550 0.479 0.746 0.765 -
RF, optimized RFVS1 0.954 0.950 0.695 0.996 0.746 0.548 0.479 0.741 0.764 0.498*
BLR, Laplace priors SVM-RFE 0.946 0.929 0.639 0.991 0.759 0.521 0.537 0.739 0.758 0.465*
BLR, Gaussian priors KW 0.926 0.856 0.557 0.980 0.728 0.525 0.426 0.701 0.713 0.043
  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.