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Table 8 The multi-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
Steel Plates Faults Accuracy 0.7464 0.7485 0.7598 0.7814 0.7881 0.7998 0.7914
OOB 0.3152 0.2819 0.2746 0.2640 0.2437 0.2276 0.2115
margin 0.2456 0.3384 0.3484 0.3789 0.3803 0.3812 0.3810
nTree 100 100 100 100 397 283 400
κ 1 5 5 27 8 6 6
num (Attribute) 27 27 27 27 23 22 22
Libras Movement Accuracy 0.7167 0.7556 0.6889 0.6444 0.7606 0.7767 0.7928
OOB 0.3546 0.3397 0.3480 0.3163 0.3030 0.3323 0.3116
margin 0.1464 0.1798 0.1990 0.2180 0.2443 0.2677 0.2910
nTree 100 100 100 100 258 348 135
κ 1 9 7 90 12 8 9
num (Attribute) 90 90 90 90 56 76 49
mfeat-fac Accuracy 0.4280 0.9030 0.8010 0.9620 0.9673 0.9600 0.9611
OOB 0.6949 0.1823 0.3192 0.0486 0.0416 0.0410 0.0361
margin −0.0987 0.4561 0.2361 0.8708 0.8749 0.8615 0.8698
nTree 100 100 100 100 377 270 196
κ 1 15 8 215 14 18 11
num (Attribute) 215 215 215 215 145 112 164
  1. The best value of every performance evaluation criteria obtained by the algorithms are marked in boldface