# Enhancing Phishing Email Detection through Ensemble Learning and Undersampling
## 5 Experimental Section
### Results for Proposed Algorithms  
Based on the experimental results presented in Tables 1 and 2, it is evident that both FMPED and FMMPED algorithms outperform the other eight previous algorithms in all evaluation metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **F1-Score** & **Accuracy** & **AUC** & **G-Mean** & **MCC** \\ \hline DT & 98.50 \(\pm\) 0.39 & 98.50 \(\pm\) 0.39 & 95.36 \(\pm\) 1.24 & 95.26 \(\pm\) 1.28 & 91.40 \(\pm\) 2.24 \\ RF & 98.75 \(\pm\) 0.13 & 98.75 \(\pm\) 0.13 & 93.88 \(\pm\) 0.62 & 93.68 \(\pm\) 0.66 & 92.71 \(\pm\) 0.76 \\ LR & 90.26 \(\pm\) **0.07** & 90.26 \(\pm\) **0.07** & 50.51 \(\pm\) 1.02 & 9.09 \(\pm\) 5.35 & 5.82 \(\pm\) 3.84 \\ GNB & 90.25 \(\pm\) 0.20 & 90.25 \(\pm\) 0.20 & 49.90 \(\pm\) **0.10** & 0.44 \(\pm\) 1.35 & 1.11 \(\pm\) 0.70 \\ MLP & 98.29 \(\pm\) 0.80 & 98.29 \(\pm\) 0.80 & 94.83 \(\pm\) 3.80 & 96.63 \(\pm\) 4.18 & 90.31 \(\pm\) 4.71 \\ KNN & 94.62 \(\pm\) 0.19 & 94.62 \(\pm\) 0.19 & 77.27 \(\pm\) 0.80 & 74.20 \(\pm\) 1.06 & 65.69 \(\pm\) 1.37 \\ SEL & 99.09 \(\pm\) 0.11 & 99.10 \(\pm\) 0.11 & 97.11 \(\pm\) 0.67 & 97.07 \(\pm\) 0.69 & 94.81 \(\pm\) 0.70 \\ SVEL & 99.33 \(\pm\) 0.10 & 99.33 \(\pm\) 0.10 & 96.78 \(\pm\) 0.46 & 96.72 \(\pm\) 0.48 & 96.17 \(\pm\) 0.58 \\ FMPED & 99.42 \(\pm\) 0.11 & 99.42 \(\pm\) 0.11 & **98.28**\(\pm\) 0.43 & **98.27**\(\pm\) 0.43 & 96.69 \(\pm\) 0.65 \\ FMMPED & **99.45**\(\pm\) 0.10 & **99.45**\(\pm\) 0.10 & 98.11 \(\pm\) 0.36 & 98.10 \(\pm\) **0.37** & **96.87**\(\pm\) **0.57** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results when the ratio of training set to test set is 4:1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **F1-Score** & **Accuracy** & **AUC** & **G-Mean** & **MCC** \\ \hline DT & 98.44 \(\pm\) 0.39 & 98.44 \(\pm\) 0.39 & 95.26 \(\pm\) 1.25 & 95.17 \(\pm\) 1.30 & 91.11 \(\pm\) 2.23 \\ RF & 98.68 \(\pm\) 0.12 & 98.68 \(\pm\) 0.12 & 93.55 \(\pm\) 0.58 & 93.33 \(\pm\) 0.62 & 92.32 \(\pm\) 0.70 \\ LR & 90.26 \(\pm\) 0.11 & 90.26 \(\pm\) 0.11 & 50.70 \(\pm\) 1.72 & 8.56 \(\pm\) 8.88 & 5.31 \(\pm\) 6.07 \\ GNB & 87.74 \(\pm\) 6.21 & 87.74 \(\pm\) 6.21 & 50.78 \(\pm\) 2.25 & 7.56 \(\pm\) 17.56 & 3.06 \(\pm\) 3.58 \\ MLP & 98.42 \(\pm\) 1.20 & 98.42 \(\pm\) 1.20 & 94.76 \(\pm\) 6.00 & 94.33 \(\pm\) 7.68 & 90.83 \(\pm\) 8.06 \\ KNN & 94.54 \(\pm\) 0.18 & 94.54 \(\pm\) 0.18 & 76.80 \(\pm\) 0.78 & 73.57 \(\pm\) 1.05 & 65.02 \(\pm\) 1.34 \\ SEL & 99.09 \(\pm\) 0.14 & 99.09 \(\pm\) 0.14 & 97.02 \(\pm\) 0.56 & 96.99 \(\pm\) 0.58 & 94.80 \(\pm\) 0.75 \\ SVEL & 99.32 \(\pm\) 0.09 & 99.32 \(\pm\) 0.09 & 97.02 \(\pm\) 0.56 & 96.99 \(\pm\) 0.58 & 96.11 \(\pm\) 0.52 \\ FMPED & 99.36 \(\pm\) 0.11 & 99.36 \(\pm\) 0.11 & **98.20**\(\pm\) 0.31 & **98.19**\(\pm\) 0.32 & 96.35 \(\pm\) 0.61 \\ FMMPED & **99.41**\(\pm\) **0.08** & **99.41**\(\pm\) **0.08** & 97.97 \(\pm\)**0.30** & 97.95 \(\pm\)**0.31** & **96.62**\(\pm\) **0.44** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results when the ratio of training set to test set is 7:3. Specifically, when the ratio of the training set to testing set is 4:1, the FMPED algorithm achieves an improvement of the AUC between 48.38% (GNB) and 1.17% (SEL), an improvement of the G-mean value between 97.83% (GNB) and 1.20% (SEL), and an improvement of the MCC between 95.58% (GNB) and 0.52% (SVEL). The FMMPED algorithm achieves an improvement of the AUC between 48.21% (GNB) and 1.00% (SEL), an improvement of the G-mean value between 97.66% (GNB) and 1.03% (SEL), and an improvement of the MCC between 95.76% (GNB) and 0.70% (SVEL). When the ratio of training set to testing set is 7:3, the FMPED algorithm achieves an improvement of the AUC between 47.50% (LR) and 1.18% (SEL), an improvement of the G-mean value between 90.63% (GNB) and 1.20% (SEL), and an improvement of the MCC between 95.99% (GNB) and 0.24% (SVEL).