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Table 8 Performance comparison of state-of-the-art classifiers on selected datasets using set of performance measures

From: A multilevel features selection framework for skin lesion classification

Classifier Dataset Performance Measures
I II III IV OA (%) Recall Precision FNR FPR AUC Time (sec)
Fine Tree \(\checkmark \)     87.5 83.0 80.5 12.5 0.17 0.84 0.76
  \(\checkmark \)    79.1 78.5 79.0 20.9 0.22 0.73 0.93
   \(\checkmark \)   82.6 72.9 72.9 17.4 0.34 0.69 1.87
    \(\checkmark \) 89.3 77.9 78.9 10.2 0.20 0.73 5.04
Medium Tree \(\checkmark \)     87.5 83.0 80.5 12.5 0.17 0.84 0.58
  \(\checkmark \)    79.1 78.5 79.0 20.9 0.22 0.73 0.76
   \(\checkmark \)   84.2 74.9 74.4 15.8 0.32 0.72 1.70
    \(\checkmark \) 87.8 69.4 75.4 11.7 0.29 0.73 2.16
Coarse Tree \(\checkmark \)     86.9 78.0 80.0 13.1 0.22 0.77 0.59
  \(\checkmark \)    80.4 79.0 80.5 19.6 0.22 0.74 0.73
   \(\checkmark \)   87.1 70.9 71.4 12.9 0.36 0.67 1.60
    \(\checkmark \) 87.5 68.4 74.4 12 0.30 0.67 2.06
Linear SVM \(\checkmark \)     93.1 87.5 90.5 6.9 0.12 0.97 0.60
  \(\checkmark \)    88.7 88.0 88.5 11.3 0.13 0.91 0.78
   \(\checkmark \)   95.2 80.9 90.9 4.8 0.26 0.91 1.78
    \(\checkmark \) 91.3 72.9 84.9 8.2 0.25 0.84 4.3
Quadratic SVM \(\checkmark \)     95.6 93.0 93.5 4.4 0.07 0.99 0.61
  \(\checkmark \)    90.9 90.5 91.0 9.1 0.10 0.93 0.78
   \(\checkmark \)   96.2 87.9 91.9 3.8 0.19 0.94 1.71
    \(\checkmark \) 94.0 82.4 88.4 5.5 0.20 0.88 4.5
Cubic SVM \(\checkmark \)     96.9 93.5 97.0 3.1 0.07 1.00 0.61
  \(\checkmark \)    91.8 91.5 91.5 8.2 0.09 0.96 0.78
   \(\checkmark \)   96.6 89.4 92.9 3.4 0.18 0.95 1.69
    \(\checkmark \) 95.6 87.4 89.4 3.9 0.21 0.90 5.42
F-KNN \(\checkmark \)     \(98.8*\) 97.0 99.0 1.2 0.03 0.97 0.60
  \(\checkmark \)    \(99.2*\) 99.0 99.0 0.8 0.02 0.94 0.73
   \(\checkmark \)   \(97.1*\) 93.9 93.4 2.9 0.13 0.88 1.62
    \(\checkmark \) 95.8 92.9 92.4 4.6 0.19 0.86 2.53
Medium KNN \(\checkmark \)     92.5 81.5 95.5 7.5 0.19 1.00 0.63
  \(\checkmark \)    91.8 91.0 91.5 8.2 0.10 0.96 0.72
   \(\checkmark \)   95.5 78.9 91.9 4.5 0.28 0.90 1.50
    \(\checkmark \) 89.3 62.4 91.4 10.2 0.35 0.86 2.15
Weighted KNN \(\checkmark \)     93.1 83.0 96.0 6.9 0.17 1.00 0.62
  \(\checkmark \)    94.4 93.5 95.0 5.6 0.07 0.98 0.72
   \(\checkmark \)   95.2 80.9 90.9 4.8 0.26 0.92 1.60
    \(\checkmark \) 94.1 75.9 87.9 5.4 0.22 0.91 2.12
Ensemble BT \(\checkmark \)     80.0 50.0 40.0 20 0.20 0.90 0.78
  \(\checkmark \)    82.6 82.0 82.5 17.4 0.19 0.83 3.47
   \(\checkmark \)   93.2 81.4 86.4 6.8 0.26 0.85 7.87
    \(\checkmark \) 92.3 73.4 88.9 7.2 0.25 0.87 13.48
Ensemble S-KNN \(\checkmark \)     98.1 95.5 99.0 1.9 0.04 1.00 4.06
  \(\checkmark \)    96.2 96.0 96.0 3.8 0.05 0.99 3.49
   \(\checkmark \)   96.8 89.9 91.4 3.2 0.17 0.92 5.36
    \(\checkmark \) \(95.9*\) 93.4 95.4 3.6 0.17 0.92 7.59
Ensemble RUSB \(\checkmark \)     88.8 86.0 81.5 11.2 0.14 0.93 4.74
  \(\checkmark \)    85.7 85.0 85.5 14.3 0.16 0.88 5.24
   \(\checkmark \)   85.5 87.9 83.4 14.5 0.21 0.88 7.09
    \(\checkmark \) 83.3 82.4 74.9 16.2 0.20 0.85 9.45
  1. * Shows the highest value in each dataset