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Table 4 Performance metrics of different ML algorithms averaged over 10 × 10-fold cross validations

From: Improved cytokine–receptor interaction prediction by exploiting the negative sample space

  SE SP ACC MCC AUC g-means
Random (10 × 10-fold cross validation)
 NB 51.04 61.22 55.49 0.106 0.560 66.2
 A1DE 72.02 52.4 62.2 0.244 0.697 61.4
 SMO-RBF 79.70 75.57 77.57 0.553 0.776 77.6
 SMO-PolyK 64.72 49.45 57.11 0.144 0.571 56.5
 SMO-PuK 71.76 83.29 78.53 0.555 0.775 77.3
 IBK 81.14 71.43 76.28 0.528 0.762 76.1
 Bagging 66.65 63.42 65.02 0.301 0.712 65.0
 RF 72.77 69.79 71.27 0.426 0.795 71.2
K-means (10 × 10-fold cross validation)
 NB 74.05 51.76 61.76 0.262 0.714 61.9
 A1DE 83.70 85.02 84.34 0.688 0.901 84.4
 SMO-RBF 88.55 90.48 89.53 0.796 0.903 89.5
 SMO-PolyK 82.72 88.06 85.39 0.709 0.847 85.3
 SMO-PuK 84.60 91.32 87.97 0.761 0.880 87.7
 IBK 84.42 86.35 85.4 0.711 0.855 85.4
 Bagging 86.10 92.25 89.19 0.786 0.938 89.1
 RF 89.42 95.98 92.52 0.853 0.957 92.6
  1. Each model was trained on different negative sample sets that have been generated using either random selection or the K-means based sampling