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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.