Skip to main content

Table 3 Dataset D: classification performance of Ph-CNN compared to other classifiers on Healthy vs. UCr classification task

From: Phylogenetic convolutional neural networks in metagenomics

UCr

Ph-CNN

LSVM

p

MCC

min CI

max CI

MCC

min CI

max CI

60

0.861

0.855

0.867

0.811

0.807

0.815

119

0.893

0.888

0.899

0.866

0.862

0.870

178

0.906

0.900

0.911

0.892

0.888

0.895

237

0.920

0.916

0.924

0.917

0.914

0.920

 

MLPNN

RF

p

MCC

min CI

max CI

MCC

min CI

max CI

60

0.873

0.869

0.443

0.797

0.792

0.801

119

0.877

0.873

0.877

0.799

0.794

0.803

178

0.859

0.855

0.880

0.791

0.787

0.794

237

0.849

0.844

0.854

0.790

0.786

0.795

  1. The performance measure is MCC, with 95% studentized bootstrap confidence intervals (min CI, max CI). Models are computed for p={25%,50%,75% and 100%} of total number of features for each task. Comparing algorithms are linear Support Vector Machines (LSVM), Random Forest (RF) and MultiLayer Perceptron (MLPNN)