Table 13 Comparison of the performance K-Nearest Neighbor classifier (KNN) using different values of k {1, 3, 5, 10} neighbors and using different distance functions; Euclidean distance, Manhattan distance and Minkowski distance without using sampling
| Â | Euclidean distance | Manhattan Distance | Minkowski Distance | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
K = 1 | K = 3 | K = 5 | K = 10 | K = 1 | K = 3 | K = 5 | K = 10 | K = 1 | K = 3 | K = 5 | K = 10 | |
Sensitivity | 28.06% | 38.19% | 42.44% | 46.78% | 28.54% | 38.21% | 42.96% | 47.59% | 28.06% | 38.19% | 42.44% | 28.06% |
Specificity | 90.24% | 89.88% | 89.50% | 89.31% | 90.28% | 89.87% | 89.49% | 89.31% | 90.24% | 89.88% | 89.50% | 90.24% |
Precision | 25.36% | 18.37% | 13.64% | 11.12% | 25.62% | 18.18% | 13.42% | 11.12% | 25.36% | 18.37% | 13.64% | 25.36% |
F-score | 26.64% | 24.81% | 20.64% | 17.97% | 27.00% | 24.64% | 20.45% | 18.03% | 26.64% | 24.81% | 20.64% | 26.64% |
RMSE | 0.4 | 0.33 | 0.32 | 0.3 | 0.4 | 0.33 | 0.32 | 0.31 | 0.4 | 0.33 | 0.32 | 0.4 |
AUC | 0.58 | 0.66 | 0.7 | 0.74 | 0.59 | 0.67 | 0.7 | 0.74 | 0.58 | 0.66 | 0.7 | 0.58 |