Table 12 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 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 | 78.43% | 65.61% | 64.17% | 50.00% | 78.29% | 65.66% | 65.68% | 61.23% | 78.43% | 65.61% | 64.17% | 59.23% |
Specificity | 96.98% | 91.74% | 90.53% | 89.84% | 97.05% | 91.80% | 90.60% | 89.91% | 96.98% | 91.74% | 90.53% | 89.84% |
Precision | 77.18% | 33.64% | 22.32% | 11.50% | 77.73% | 34.16% | 22.94% | 16.44% | 77.18% | 33.64% | 22.32% | 15.89% |
F-score | 77.80% | 44.47% | 33.12% | 18.70% | 78.01% | 44.94% | 34.01% | 25.91% | 77.80% | 44.47% | 33.12% | 25.05% |
RMSE | 0.23 | 0.27 | 0.28 | 0.29 | 0.23 | 0.27 | 0.28 | 0.29 | 0.23 | 0.27 | 0.28 | 0.29 |
AUC | 0.88 | 0.86 | 0.85 | 0.84 | 0.87 | 0.86 | 0.85 | 0.84 | 0.87 | 0.86 | 0.85 | 0.84 |