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