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Table 4 Classification results based on different feature subsets using three methods

From: Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine

Model ML method Feature subset selection method Feature number Classification results (%)
SE SP Acc Gm
miPlantPre NBC PCA 76 92.2 92.6 92.4 92.4
CFS 20 93.9 97.8 95.8 95.8
B-SVM-RFE 47 93.8 98.6 96.2 96.2
All features 152 92.9 98.0 95.4 95.4
RF PCA 76 93.5 95.3 94.4 94.4
CFS 20 95.0 97.6 96.3 96.3
B-SVM-RFE 47 95.3 97.7 96.5 96.5
All features 152 95.3 97.7 96.5 96.5
SVM PCA 76 94.9 99.2 97.0 97.0
CFS 20 94.3 99.1 96.7 96.7
B-SVM-RFE 47 95.5 99.1 97.2 97.2
All features 152 93.9 98.5 96.2 96.2
miPlantMat NBC PCA 71 88.6 82.3 85.5 85.4
CFS 40 93.2 74.8 83.6 83.5
B-SVM-RFE 63 89.8 88.4 89.1 89.1
All features 152 91.7 79.3 85.5 85.3
RF PCA 71 93.2 73.2 83.2 82.6
CFS 40 89.2 89.1 89.2 89.2
B-SVM-RFE 63 89.7 88.6 89.2 89.2
All features 152 86.6 84.4 85.5 85.5
SVM PCA 71 88.6 84.3 86.4 86.4
CFS 40 90.6 87.5 89.1 89.1
B-SVM-RFE 63 92.9 88.7 90.8 90.8
All features 152 87.1 81.6 84.4 84.4