Skip to main content

Table 7 Dataset D: classification performance of Ph-CNN compared to other classifiers on Healthy vs. iCDr classification task

From: Phylogenetic convolutional neural networks in metagenomics

iCDr

Ph-CNN

LSVM

p

MCC

min CI

max CI

MCC

min CI

max CI

65

0.753

0.744

0.763

0.773

0.769

0.779

129

0.830

0.823

0.837

0.834

0.830

0.837

193

0.884

0.878

0.889

0.893

0.891

0.896

257

0.910

0.905

0.915

0.907

0.904

0.909

 

MLPNN

RF

p

MCC

min CI

max CI

MCC

min CI

max CI

63

0.807

0.802

0.812

0.724

0.719

0.729

125

0.822

0.816

0.827

0.794

0.788

0.800

188

0.831

0.827

0.835

0.812

0.807

0.818

250

0.837

0.831

0.842

0.820

0.816

0.825

  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)