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Table 1 Comparing different combinations of methods for imbalanced data and learning algorithms. A 10-fold cross validation was performed in each case using TSHA1, TSHA2, and TSHA3 for the respective stages

From: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

Method 1st stage 2nd stage 3rd stage
  SN SL GM SN SL GM SN SL GM
LibSVM:          
Cost matrix 4.7 64.7 17.4 3.8 81.8 17.7 4.2 100 20.6
Sampling 99.6 55.4 74.3 99.6 54.8 73.8 100 65 80.6
SMOTE 87.5 99.6 93.4 82.5 100 90.8 97.9 100 98.9
SMO:          
Cost matrix 80.9 17.9 38 85.6 40 58.1 97.9 77.5 87.1
Sampling 84.3 77.7 81 86 90.6 88.3 98.7 96.7 97.7
SMOTE 86.3 83.5 84.9 91.9 94.4 93.1 99.9 99.3 99.6
MLP:          
Cost matrix 74.1 16.7 35.2 79.2 49.7 62.7 98.7 3.9 19.6
Sampling 78.8 77.5 78.2 89.8 88.3 89.1 97 97.9 97.4
SMOTE 91.5 90.8 91.1 98 97.1 97.5 99.9 99.7 99.8
RF:          
Cost matrix 46.2 44 45.1 74.6 76.5 75.5 89 89 89
Sampling 84.3 78.7 81.4 87.7 87.7 87.7 97.9 97.1 97.5
SMOTE 98.2 96.3 97.2 99.1 98.4 98.7 99.9 99.4 99.7