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Table 2 Dataset D: classification performance of Ph-CNN compared to other classifiers on Healthy vs. UCf classification task

From: Phylogenetic convolutional neural networks in metagenomics

UCf Ph-CNN LSVM
p MCC min CI max CI MCC min CI max CI
63 0.794 0.785 0.803 0.799 0.793 0.803
125 0.852 0.845 0.860 0.861 0.857 0.865
188 0.920 0.916 0.925 0.924 0.921 0.926
250 0.940 0.937 0.944 0.943 0.941 0.945
  MLPNN RF
p MCC min CI max CI MCC min CI max CI
63 0.701 0.692 0.721 0.729 0.723 0.736
125 0.838 0.834 0.842 0.843 0.837 0.849
188 0.865 0.861 0.869 0.902 0.899 0.906
250 0.898 0.894 0.901 0.903 0.900 0.907
  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)