Table 3.
Sensitivity, Specificity, and G-mean for each of the datasets
| Sensitivity | Specificity | G-mean | ||||
|---|---|---|---|---|---|---|
|
Datasets |
SMOTE |
LVQ-SMOTE |
SMOTE |
LVQ-SMOTE |
SMOTE |
LVQ-SMOTE |
| Breast-w |
76.40% |
74.16% |
64.21% |
67.89% |
70.31% |
71.03% |
| Blood |
95.44% |
95.00% |
97.38% |
99.04% |
96.41% |
97.02% |
| Colon-cancer |
80.00% |
85.00% |
63.64% |
72.73% |
71.82% |
78.86% |
| Ionosphere |
80.16% |
86.51% |
91.56% |
92.44% |
85.86% |
89.48% |
| Leukemia |
95.65% |
100.0% |
95.92% |
100.0% |
95.79% |
100.0% |
| Pima |
72.76% |
71.27% |
77.60% |
80.20% |
75.18% |
75.73% |
| Satimage |
78.75% |
75.76% |
68.53% |
75.67% |
73.64% |
75.71% |
| Yeast | 74.51% | 71.72% | 86.81% | 90.81% | 80.66% | 81.27% |
This is the case of Logistic Tree which has shown the highest G-mean among the basic classification algorithms in Table 2.