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Table 6 Performance of HAFS achieved by SVM, KNN, GaussianNB and QDA by using and not using HAFS

From: Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images

Method \(\alpha \) Label Precision (%) Recall (%) Accuracy (%) F-measure (%)
SVM   1 76.34 99.42 82.3 86.37
  2 99.48 62.07 92.31 76.45
  3 100.0 57.75 98.04 73.21
  4 96.84 58.2 91.75 72.71
HAFS-SVM 0.1 1 90.08 95.03 91.3 92.49
2 91.64 87.03 95.8 89.28
3 99.1 77.46 98.92 86.96
4 84.98 80.14 93.58 82.49
KNN   1 88.04 86.32 85.66 87.17
  2 83.09 83.23 93.19 83.16
  3 78.05 86.49 98.17 82.05
  4 65.98 67.96 87.58 66.96
HAFS-KNN 0.5 1 89.01 87.01 86.6 88.0
2 83.17 84.52 93.42 83.84
3 80.77 85.14 98.31 82.89
4 66.89 69.37 87.97 68.11
GaussianNB   1 88.57 59.6 72.88 71.25
  2 42.62 62.72 75.52 50.75
  3 14.64 86.62 76.01 25.05
  4 37.82 10.19 79.89 16.05
HAFS-GaussianNB 0.1 1 81.77 77.8 77.71 79.74
2 46.21 51.38 78.19 48.66
3 34.96 55.63 93.16 42.93
4 46.67 41.11 80.02 43.71
QDA   1 95.14 36.67 63.72 52.94
  2 39.1 97.27 66.82 55.78
  3 100.0 99.3 99.97 99.65
  4 43.38 48.66 79.07 45.87
HAFS-QDA 0.3 1 86.07 82.19 82.69 84.09
2 60.85 82.88 84.84 70.17
3 58.93 92.96 96.68 72.13
4 56.51 31.84 83.12 40.73