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Table 2 Average G-mean for three cases

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

 

G-mean

 

Classification algorithm

Nothing : base line

AdaboostM1

SMOTE

LVQ-SMOTE

NaiveBayes

76.25%

77.34%

78.54%

78.94%

Logistic Tree

72.88%

74.21%

81.21%

83.64%

Neural Network

75.24%

79.62%

80.44%

80.24%

SVM

72.65%

73.31%

80.92%

83.22%

RandomForest

75.34%

78.96%

79.47%

80.68%

OLVQ3

75.76%

74.35%

80.88%

82.55%

  1. Nothing represents that all the datasets were remained as the class imbalanced problem. In the case of SMOTE and LVQ-SMOTE, the minority samples were increased up to the number of the majority samples.
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BioData Mining

ISSN: 1756-0381