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Table 5 Standard errors of different performance metrics for 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 0.0201 0.0178 0.0114 0.0156 0.0113 0.0113 0.0226 0.0234 0.0036 0.0409 0.0282 0.0474 0.0108 0.0334
BS 0.0146 0.0149 0.0111 0.0125 0.0113 0.0112 0.0224 0.0177 0.3156 0.0334 0.0218 0.0715 0.1652 0.0504
LG 0.0569 0.0740 0.0601 0.0575 0.0624 0.0621 0.1238 0.0065 0.0056 0.0121 0.0076 0.0313 0.0045 0.0267
NB 0.0630 0.0826 0.0560 0.0571 0.0573 0.0577 0.1169 0.0357 0.1043 0.0500 0.0390 0.0439 0.0549 0.1579
KN 0.1502 0.1279 0.1386 0.1454 0.1364 0.1354 0.2701 0.0221 0.3023 0.0389 0.0267 0.0765 0.1595 0.0799
P-2 BG 0.0201 0.0272 0.0192 0.0188 0.0201 0.0200 0.0397 0.0261 0.0060 0.0429 0.0310 0.0421 0.0130 0.0364
BS 0.0207 0.0179 0.0161 0.0184 0.0163 0.0163 0.0327 0.0273 0.0033 0.0456 0.0325 0.0461 0.0134 0.0358
LG 0.0688 0.0799 0.0617 0.0644 0.0630 0.0632 0.1272 0.0182 0.0148 0.0290 0.0214 0.0273 0.0107 0.0410
NB 0.0546 0.0629 0.0421 0.0472 0.0405 0.0407 0.0824 0.0316 0.0733 0.0487 0.0363 0.0436 0.0426 0.1342
KN 0.0925 0.0811 0.0362 0.0681 0.0266 0.0280 0.0598 0.0235 0.0044 0.0337 0.0269 0.0282 0.0117 0.0270
P-3 BG 0.0156 0.0186 0.0117 0.0130 0.0120 0.0119 0.0237 0.0185 0.0052 0.0291 0.0217 0.0267 0.0089 0.0235
BS 0.0121 0.0178 0.0102 0.0102 0.0108 0.0107 0.0210 0.0194 0.0039 0.0324 0.0231 0.0323 0.0095 0.0256
LG 0.0406 0.0586 0.0376 0.0377 0.0409 0.0402 0.0795 0.0210 0.0116 0.0334 0.0247 0.0303 0.0132 0.0440
NB 0.0380 0.0689 0.0330 0.0323 0.0372 0.0368 0.0735 0.0254 0.0434 0.0397 0.0295 0.0333 0.0286 0.0913
KN 0.1017 0.0829 0.0629 0.0842 0.0566 0.0544 0.1076 0.0292 0.0078 0.0504 0.0352 0.0629 0.0116 0.0334
  1. MD methods, EP encoding procedures, BG bagging, BS boosting, LG logistic regression, NB naïve bayes, KN K nearest neighbor