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Table 6 P-values of the Mann Whitney statistic to test the significant difference between the performance of RF with that of Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers in all the three encoding procedures under both balanced and imbalanced situations

From: Prediction of donor splice sites using random forest with a new sequence encoding approach

$D EP CLs TPR TNR F (α = 1) F (β = 2) G-mean WA MCC
Balanced P-1 RF-BG 0.343066 0.676435 0.272856 0.212122 0.185711 0.240436 0.272856
RF-BS 0.820063 0.939006 0.314999 0.795936 0.314999 0.383598 0.314999
RF-LG 0.001672 0.053092 0.002879 0.000725 0.005196 0.009082 0.005196
RF-NB 0.000242 0.002796 1.08E-05 1.08E-05 1.08E-05 0.000181 1.08E-05
RF-KN 0.053182 0.087051 0.028806 0.063013 0.035463 0.025581 0.028806
P-2 RF-BG 0.41319 0.594314 0.356232 0.356232 0.277512 0.315378 0.356232
RF-BS 0.837765 0.367844 0.968239 0.968239 0.842105 0.743537 0.842105
RF-LG 0.000275 0.000439 2.17E-05 2.17E-05 2.17E-05 2.17E-05 2.17E-05
RF-NB 0.000275 0.004216 2.17E-05 2.17E-05 2.17E-05 2.17E-05 2.17E-05
RF-KN 0.000376 0.000273 2.17E-05 2.17E-05 2.17E-05 0.000278 2.17E-05
P-3 RF-BG 0.171672 0.879378 0.14314 0.14314 0.165494 0.15062 0.14314
RF-BS 0.494174 0.381613 0.970512 0.528849 0.853428 0.820197 0.911797
RF-LG 0.000181 0.000181 1.08E-05 1.08E-05 1.08E-05 1.08E-05 1.08E-05
RF-NB 0.000182 0.000279 1.08E-05 1.08E-05 1.08E-05 0.000182 1.08E-05
RF-KN 0.000182 0.000181 1.08E-05 1.08E-05 1.08E-05 0.000182 1.08E-05
Imbalanced P-1 RF-BG 0.000269 0.000251 2.17E-05 2.17E-05 2.17E-05 0.000278 2.17E-05
RF-BS 0.000176 0.002555 0.000181 0.000181 0.000181 0.000178 0.000181
RF-LG 0.000263 0.000268 2.17E-05 2.17E-05 2.17E-05 0.000263 2.17E-05
RF-NB 0.000271 0.177338 2.17E-05 2.17E-05 2.17E-05 2.17E-05 2.17E-05
RF-KN 0.000175 0.025526 1.08E-05 1.08E-05 1.08E-05 0.000182 0.000179
P-2 RF-BG 0.000179 0.000173 0.000182 0.000182 0.000182 0.000181 0.000182
RF-BS 0.000181 0.000158 1.08E-05 1.08E-05 1.08E-05 0.000181 1.08E-05
RF-LG 0.00018 0.000178 1.08E-05 1.08E-05 1.08E-05 0.00018 1.08E-05
RF-NB 0.000182 0.733634 1.08E-05 1.08E-05 1.08E-05 0.000182 1.08E-05
RF-KN 0.000181 0.000174 1.08E-05 1.08E-05 1.08E-05 0.000182 1.08E-05
P-3 RF-BG 0.000176 0.000168 0.000182 0.000182 0.000182 0.000181 0.000182
RF-BS 0.000179 0.000149 1.08E-05 1.08E-05 1.08E-05 1.08E-05 1.08E-05
RF-LG 0.000179 0.000177 1.08E-05 1.08E-05 1.08E-05 0.000182 1.08E-05
RF-NB 0.000177 0.009082 1.08E-05 1.08E-05 1.08E-05 1.08E-05 1.08E-05
RF-KN 0.00018 0.00018 1.08E-05 1.08E-05 1.08E-05 0.000178 1.08E-05
  1. $D data type, RF random forest, CLs classifiers, BG bagging, BS boosting, LG logistic regression, NB naïve bayes, KN K nearest neighbor