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Table 4 Performance metrics of Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for 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

EP MD Balanced Imbalanced
   TPR TNR F (α = 1) F (β = 2) G-mean WA MCC TPR TNR F (α = 1) F (β = 2) G-mean WA MCC
P-1 BG 0.944 0.921 0.934 0.940 0.933 0.933 0.866 0.069 0.996 0.127 0.084 0.258 0.533 0.172
BS 0.952 0.919 0.936 0.945 0.935 0.935 0.872 0.041 0.898 0.079 0.051 0.192 0.470 0.129
LG 0.895 0.882 0.889 0.892 0.888 0.888 0.777 0.008 0.993 0.016 0.010 0.087 0.502 0.012
NB 0.835 0.836 0.836 0.835 0.834 0.835 0.674 0.202 0.838 0.297 0.231 0.409 0.520 0.067
KN 0.856 0.840 0.847 0.852 0.847 0.848 0.697 0.048 0.854 0.087 0.058 0.200 0.451 0.012
P-2 BG 0.927 0.882 0.907 0.919 0.904 0.904 0.810 0.112 0.992 0.198 0.135 0.330 0.552 0.216
BS 0.934 0.901 0.918 0.928 0.917 0.917 0.835 0.090 0.996 0.163 0.109 0.296 0.543 0.200
LG 0.742 0.734 0.739 0.741 0.737 0.738 0.478 0.112 0.981 0.198 0.135 0.330 0.547 0.190
NB 0.772 0.758 0.767 0.770 0.764 0.765 0.532 0.159 0.884 0.250 0.186 0.373 0.521 0.073
KN 0.813 0.678 0.760 0.790 0.739 0.746 0.502 0.173 0.981 0.290 0.207 0.412 0.577 0.262
P-3 BG 0.924 0.904 0.915 0.920 0.914 0.914 0.828 0.125 0.991 0.220 0.151 0.351 0.558 0.230
BS 0.941 0.898 0.922 0.933 0.920 0.920 0.841 0.095 0.995 0.171 0.115 0.305 0.545 0.205
LG 0.813 0.775 0.798 0.807 0.793 0.794 0.589 0.120 0.983 0.210 0.144 0.342 0.551 0.202
NB 0.784 0.761 0.775 0.780 0.771 0.772 0.547 0.178 0.945 0.289 0.210 0.410 0.562 0.196
KN 0.795 0.700 0.756 0.778 0.742 0.747 0.501 0.065 0.989 0.120 0.080 0.247 0.527 0.142
  1. MD methods, EP encoding procedures, BG bagging, BS boosting, LG logistic regression, NB naïve bayes, KN K nearest neighbor