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Table 6 Discrimination of channels and pores using different machine learning approaches

From: Functional discrimination of membrane proteins using machine learning techniques

Method 5-fold cross-validation
  Sensitivity (%) Specificity (%) F-measure Accuracy (%)
    Channel Pore  
Bayesnet 94.1 81.4 0.910 0.857 88.9
Naive Bayes 92.5 88.4 0.923 0.887 90.8
Logistic function 92.0 89.1 0.922 0.888 90.8
Neural network 93.0 91.5 0.935 0.915 92.4
RBF network 92.5 88.4 0.923 0.887 90.8
Support vector machines 95.2 88.4 0.937 0.905 92.4
k-nearest neighbor 89.8 86.8 0.903 0.862 88.6
Bagging meta learning 89.8 83.7 0.894 0.844 87.3
Classification via Regression 88.2 85.3 0.889 0.843 87.0
Decision tree J4.8 86.1 78.3 0.856 0.789 82.9
NBTree 90.9 83.7 0.899 0.850 88.0
Partial decision tree 87.2 79.1 0.865 0.800 83.9