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Table 4 Performance achieved by DNN, NB, kNN, RF and SVM measured using MCC as evaluation metric

From: Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Dataset Algorithm MCC 1st MCC 2nd MCC 3rd Mean MCC Std MCC
DRD4 DNN 0.900 0.895 0.867 0.887 0.018
SVM_rbf 0.889 0.876 0.865 0.876 0.012
SVM_linear 0.828 0.816 0.840 0.828 0.012
RF 0.867 0.854 0.862 0.861 0.007
kNN 0.762 0.778 0.763 0.767 0.009
NB 0.742 0.761 0.750 0.751 0.009
HERG DNN 0.858 0.913 0.880 0.884 0.028
SVM_rbf 0.845 0.891 0.872 0.869 0.023
SVM_linear 0.773 0.782 0.780 0.778 0.005
RF 0.838 0.857 0.848 0.847 0.010
kNN 0.818 0.813 0.825 0.819 0.006
NB 0.612 0.620 0.602 0.611 0.009
CDK2 DNN 0.919 0.932 0.927 0.926 0.007
SVM_rbf 0.920 0.913 0.922 0.919 0.005
SVM_linear 0.863 0.889 0.864 0.872 0.015
RF 0.895 0.912 0.902 0.903 0.008
kNN 0.895 0.904 0.910 0.903 0.007
NB 0.769 0.773 0.780 0.774 0.006
CogX DNN 0.982 0.981 0.987 0.983 0.003
SVM_rbf 0.978 0.979 0.980 0.979 0.001
SVM_linear 0.971 0.970 0.977 0.973 0.004
RF 0.973 0.979 0.982 0.978 0.004
kNN 0.968 0.971 0.971 0.970 0.002
NB 0.889 0.897 0.882 0.889 0.008
CYP_19A1 DNN 0.893 0.920 0.899 0.904 0.014
SVM_rbf 0.886 0.896 0.889 0.890 0.005
SVM_linear 0.849 0.866 0.862 0.859 0.009
RF 0.873 0.910 0.879 0.887 0.020
kNN 0.805 0.811 0.821 0.812 0.008
NB 0.755 0.821 0.775 0.784 0.034
CB1 DNN 0.943 0.941 0.940 0.941 0.002
SVM_rbf 0.941 0.937 0.931 0.936 0.005
SVM_linear 0.885 0.893 0.881 0.886 0.007
RF 0.908 0.923 0.914 0.915 0.008
kNN 0.906 0.921 0.901 0.909 0.011
NB 0.758 0.781 0.765 0.768 0.012
CAII DNN 0.858 0.885 0.843 0.862 0.021
SVM_rbf 0.857 0.851 0.866 0.858 0.007
SVM_linear 0.828 0.826 0.830 0.828 0.002
RF 0.836 0.857 0.861 0.851 0.013
kNN 0.558 0.557 0.577 0.564 0.011
NB 0.754 0.769 0.783 0.769 0.015
  1. Results for each activity class and validation set from three experiments as shown. Best recorded results for each activity class are highlighted in italic