Table 4 Classification results for the information theft dataset with sampling and tuned hyperparameters experiments; mean and standard deviations of AUC and AUPRC, (10 iterations of 5-fold cross-validation)
From: IoT information theft prediction using ensemble feature selection
Experiment name | Mean | SD | Mean | SD |
|---|---|---|---|---|
AUC | AUC | AUPRC | AUPRC | |
CatBoost Tuned RUS 1:3 | 0.99727 | 0.00188 | 0.99980 | 0.00032 |
CatBoost Tuned RUS 1:1 | 0.99803 | 0.00149 | 0.99979 | 0.00035 |
DT Tuned RUS 1:3 | 0.99362 | 0.00330 | 0.99524 | 0.00281 |
DT Tuned RUS 1:1 | 0.99357 | 0.00395 | 0.99337 | 0.00424 |
Light GBM Tuned RUS 1:3 | 0.99539 | 0.00305 | 0.99915 | 0.00106 |
Light GBM Tuned RUS 1:1 | 0.99569 | 0.00240 | 0.99925 | 0.00073 |
LR Tuned RUS 1:3 | 0.99495 | 0.00241 | 0.99872 | 0.00082 |
LR Tuned RUS 1:1 | 0.99499 | 0.00231 | 0.99658 | 0.01024 |
MLP Tuned RUS 1:3 | 0.99398 | 0.00372 | 0.99782 | 0.00188 |
MLP Tuned RUS 1:1 | 0.99420 | 0.00363 | 0.99710 | 0.00555 |
NB Tuned RUS 1:3 | 0.99266 | 0.00254 | 0.97693 | 0.00522 |
NB Tuned RUS 1:1 | 0.99247 | 0.00276 | 0.97723 | 0.00736 |
RF Tuned RUS 1:3 | 0.99671 | 0.00213 | 0.99960 | 0.00060 |
RF Tuned RUS 1:1 | 0.99719 | 0.00181 | 0.99967 | 0.00051 |
XGBoost Tuned RUS 1:3 | 0.99688 | 0.00198 | 0.99957 | 0.00071 |
XGBoost Tuned RUS 1:1 | 0.99711 | 0.00183 | 0.99957 | 0.00063 |