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Table 3 Classifier accuracies (ranks) for each class, the Friedman statistic (T1) and average classifier rank, for the experiments with the 4 datasets

From: An artificial neural networks approach for assessment treatment response in oncological patients using PET/CT images

Dataset Classifier Negative Neutral Positive (partial) Positive (complete) Average rank
Original T1=1.956 kNN 0 (4.5) 0.167 (5.5) 0.741 (4) 0.893 (4)
  LVQNN 0 (4.5) 1.0 (1) 1.0 (1) 0.929 (2)
  MLPI 1.0 (1) 0.667 (2) 0.778 (3) 0.893 (4)
  MLPII 0.5 (2) 0.5 (3) 0.889 (2) 0.893 (4)
  RBFNN 0 (4.5) 0.167 (5.5) 0.444 (6) 0.964 (1)
  PNN 0 (4.5) 0.333 (4) 0.704 (5) 0.750 (6)
SMOTE T1=12 kNN 0.125 (6) 0.417 (6) 0.630 (4.5) 0.893 (4) 5.125
  LVQNN 1.0 (1.5) 1.0 (1) 1.0 (1) 1.0 (1) 1.125
  MLPI 0.5 (3) 0.5 (5) 0.778 (3) 0.930 (2.5) 3.375
  MLPII 1.0 (1.5) 0.917 (2) 0.852 (2) 0.930 (2.5) 2
  RBFNN 0.25 (5) 0.667 (4) 0.444 (6) 0.393 (6) 5.25
  PNN 0.375 (4) 0.889 (3) 0.630 (4.5) 0.679 (5) 4.125
PCA T1=4.241 kNN 0 (4.5) 0.167 (5.5) 0.741 (5) 0.893 (3.5) 4.625
  LVQNN 0 (4.5) 1.0 (1) 1.0 (1) 0.929 (1.5) 2
  MLPI 0.5 (1.5) 0.5 (2.5) 0.778 (3.5) 0.929 (1.5) 2.25
  MLPII 0.5 (1.5) 0.5 (2.5) 0.889 (2) 0.893 (3.5) 2.375
  RBFNN 0 (4.5) 0.333 (4) 0.667 (6) 0.464 (6) 5.125
  PNN 0 (4.5) 0.167(5.5) 0.778 (3.5) 0.821 (5) 4.625
SMOTE+PCA T1=4.602 KNN 0.5 (3.5) 0.67 (4) 0.630 (5) 0.893 (4) 4.125
  LVQNN 1.0 (1) 1.0 (1) 0.963 (1) 1.0 (1) 1.0
  MLP 0.125 (6) 0.833 (2.5) 0.704 (4) 0.964 (2.5) 3.75
  MLPII 0.5 (3.5) 0.833 (2.5) 0.815 (2) 0.964 (2.5) 2.625
  RBFNN 0.375 (5) 0.417 (6) 0.741 (3) 0.714 (5) 4.75
  PNN 0.625 (2) 0.583 (5) 0.556 (6) 0.679 (6) 4.75