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Table 3 Sensitivity, Specificity, and G-mean for each of the datasets

From: LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data

  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%
  1. This is the case of Logistic Tree which has shown the highest G-mean among the basic classification algorithms in Table 2.