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Table 7 The binary classification results

From: CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests

   1 \( \left\lfloor \sqrt{M}\right\rfloor \)  log2(M) + 1 M GA-RF PSO-RF AFSA-RF
Connectionist Bench Accuracy 0.6442 0.6442 0.6058 0.6635 0.6538 0.7308 0.6827
Sensitive 0.5882 0.6122 0.6500 0.7556 0.5741 0.6744 0.5870
Precision 0.6522 0.6250 0.4906 0.5862 0.7045 0.6744 0.6585
Specificity 0.6981 0.6727 0.5781 0.5932 0.7400 0.7705 0.7586
F 0.6186 0.6186 0.5591 0.6602 0.6327 0.6744 0.6207
G-mean 0.6408 0.6418 0.6130 0.6695 0.6518 0.7209 0.6673
AUC 0.4107 0.4119 0.3758 0.4482 0.4248 0.5196 0.4453
OOB 0.3808 0.3889 0.3344 0.3391 0.3314 0.3085 0.2932
margin 0.1078 0.1632 0.1991 0.2084 0.2056 0.1468 0.2418
nTree 100 100 100 100 315 193 151
κ 1 4 5 17 6 8 4
num (Attribute) 17 17 17 17 13 16 15
Wine Accuracy 0.9846 0.9692 0.9846 0.9692 0.9846 0.9846 0.9692
Sensitive 1.0000 0.9286 1.0000 1.0000 1.0000 1.0000 1.0000
Precision 0.9655 1.0000 0.9677 0.9333 0.9706 0.9643 0.9355
Specificity 0.9730 1.0000 0.9714 0.9459 0.9688 0.9737 0.9444
F 0.9825 0.9630 0.9836 0.9655 0.9851 0.9818 0.9667
G-mean 0.9864 0.9636 0.9856 0.9726 0.9843 0.9868 0.9718
AUC 0.9730 0.9286 0.9714 0.9459 0.9688 0.9737 0.9444
OOB 0.0442 0.0502 0.0288 0.0748 0.0246 0.0156 0.0238
margin 0.6951 0.7553 0.8149 0.7995 0.7863 0.7890 0.8345
nTree 100 100 100 100 349 354 90
κ 1 3 4 13 5 1 5
num (Attribute) 13 13 13 13 12 11 12
Ionosphere Accuracy 0.9200 0.9314 0.9371 0.9257 0.9371 0.9257 0.9314
Sensitive 0.9107 0.8475 0.8889 0.8824 0.8333 0.9032 0.9107
Precision 0.8500 0.9434 0.9057 0.9231 0.9804 0.8889 0.8793
Specificity 0.9244 0.9741 0.9587 0.9533 0.9913 0.9381 0.9412
F 0.8793 0.8929 0.8972 0.9003 0.9009 0.8960 0.8947
G-mean 0.9175 0.9086 0.9231 0.9171 0.9089 0.9205 0.9258
AUC 0.8956 0.8651 0.9002 0.8975 0.8548 0.8835 0.9029
OOB 0.1096 0.0860 0.1132 0.0884 0.0668 0.0831 0.0825
margin 0.5696 0.6918 0.6511 0.7041 0.7349 0.6934 0.6351
nTree 100 100 100 100 339 321 350
κ 1 5 6 34 9 15 2
num (Attribute) 34 34 34 34 29 30 28
Breast -cancer -wisconsin Accuracy 0.9801 0.9658 0.9715 0.9573 0.9544 0.9801 0.9658
Sensitive 0.9914 0.9474 0.9583 0.9748 0.9919 1.0000 0.9474
Precision 0.9504 0.9474 0.9583 0.9063 0.8905 0.9421 0.9474
Specificity 0.9745 0.9747 0.9784 0.9483 0.9342 0.9705 0.9747
F 0.9701 0.9474 0.9583 0.9393 0.9385 0.9702 0.9474
G-mean 0.9829 0.9609 0.9683 0.9614 0.9626 0.9851 0.9609
AUC 0.9844 0.9555 0.9595 0.9547 0.9601 0.9850 0.9474
OOB 0.0422 0.0399 0.0433 0.0467 0.0304 0.0411 0.0372
margin 0.8247 0.8569 0.8509 0.8652 0.8842 0.8179 0.8616
nTree 100 100 100 100 319 420 351
κ 1 3 4 10 3 1 3
num (Attribute) 10 10 10 10 9 9 7
  1. The best value of every performance evaluation criteria obtained by the algorithms are marked in boldface