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Table 4 Performance comparison of five commonly used binary classifiers on proposed features

From: TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings

Classifier Cross-validation data
  Acc (%) Spec (%) Sen (%) MCC
SVM 95.82 ± 1.67 97.59 ± 2.15 83.67 ± 7.45 0.83 ± 0.06
kNN 77.33 ± 3.7 75.41 ± 3.98 100 ± 0 0.47 ± 0.03
RandomForest 94.22 ± 2.3 94.20 ± 2.9 94 ± 8.43 0.75 ± 0.05
Naïve Bayes 21.59 ± 10.62 14.76 ± 11.45 100 ± 0 0.09 ± 0.06
QuickRBF 94.80 ± 1.52 99.81 ± 0.4 57.99 ± 14.25 0.72 ± 0.09
  Independent data
  Acc (%) Spec (%) Sen (%) MCC
SVM 96.49 ± 4.34 98 ± 5.27 85 ± 17.48 0.86 ± 0.13
kNN 79.39 ± 8.9 78.01 ± 10.57 93.34 ± 14.04 0.47 ± 0.09
RandomForest 97.28 ± 2.25 99 ± 2.26 80.01 ± 23.31 0.84 ± 0.14
Naïve Bayes 19.09 ± 23.76 10.99 ± 26.15 100 ± 0 0.08 ± 0.17
QuickRBF 94.12 ± 1.97 100 ± 0 50 ± 16.7 0.68 ± 0.13
  1. (Each result is reported in format: m ± d, where m is the mean and d is the standard deviation across the ten runs)