# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 3 Results and Discussion
### Performance of the Model for Different Classifiers  
We conducted a comprehensive comparison using different classifiers to identify the best classifier to build AFP_MVLF. The results of the comparison between the classifiers for 10-fold cross-validation and the independent test set are presented in Tables 2 and 3 for the dataset Antifp_DS1, respectively. Note that for this comparison, we used all the extracted features using iFeature (no feature extraction). As shown in these tables, RF performs better than other classifiers used in this study. Using RF, we achieve an accuracy of 93.5%, F1 score of 0.92, precision of 91.2%, and MCC of 0.89 for the 10-fold cross-validation. RF also stands out with the highest accuracy of 93.8%, F1 score of 0.93, precision of 96.6%, and MCC of 0.80 for the independent test dataset. As shown in Tables 4 and 5, again, RF consistently delivered the top performance among all classifiers for the Antifp_DS2 dataset using a 10-fold cross-validation and independent test set, respectively. It achieves remarkable accuracy of (93.5% and 93.1%), F1 scores of (0.93 and 0.93), MCC scores of (0.87 and 0.86), and precision scores of (92.6% and 92.3%), respectively, using 10-fold cross-validation and the independent test dataset. We also conducted the comparison for the Antifp_DS3 dataset and the summarized result in Tables 6 and 7 for a 10-fold cross-validation and independent test set, respectively. As shown in these tables, the model constructed with the RF performs the best among all the other classifiers. The model outperformed, resulting in ACC values of 93.7% and 94.1%, F1 scores of 0.93 and 0.92, MCC scores of 0.82 and 0.87, and precision scores of 94.3% and 95.1%, respectively. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algo** & **Acc (\%)** & **F1** & **MCC** & **Precision (\%)** & **SN (\%)** & **SE (\%)** \\ \hline SVM & 93.5 & 0.93 & 0.87 & 92.6 & 90.5 & 91.4 \\ \hline
**RF** & **96.4** & **0.95** & **0.92** & **97.7** & **93.2** & **93.8** \\ \hline AdaBoost & 92.6 & 0.91 & 0.90 & 89.0 & 89.5 & 91.1 \\ \hline NB & 88.9 & 0.89 & 0.82 & 86.3 & 80.2 & 78.8 \\ \hline LR & 92.5 & 0.91 & 0.85 & 92.8 & 90.1 & 90.5 \\ \hline SGD & 90.9 & 0.91 & 0.81 & 90.9 & 88.3 & 86.1 \\ \hline Bernoulli NB & 91.1 & 0.90 & 0.82 & 93.9 & 88.3 & 87.6 \\ \hline DT & 91.6 & 0.91 & 0.83 & 90.8 & 89.5 & 90.0 \\ \hline RT & 92.5 & 0.92 & 0.92 & 92.2 & 92.3 & 91.7 \\ \hline \end{tabular}
\end{table} 
Table 4: The results of comparing machine learning algorithms based on various performance metrics using 10-fold cross-validation for the Antifp_DS2 dataset. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Algo** & **Acc (\%)** & **F1** & **MCC** & **Precision (\%)** & **SN (\%)** & **SE (\%)** \\ \hline SVM & 91.5 & 0.91 & 0.83 & 91.2 & 90.5 & 91.4 \\ \hline
**RF** & **93.5** & **0.92** & **0.89** & **93.1** & **93.2** & **93.8** \\ \hline AdaBoost & 91.2 & 0.93 & 0.90 & 90.2 & 89.5 & 91.1 \\ \hline NB & 78.4 & 0.77 & 0.57 & 81.4 & 80.2 & 78.8 \\ \hline LR & 90.9 & 0.90 & 0.81 & 90.5 & 90.1 & 90.5 \\ \hline SGD & 89.3 & 0.88 & 0.80 & 89.1 & 88.3 & 86.1 \\ \hline Bernoulli NB & 87.4 & 0.87 & 0.74 & 88.7 & 88.3 & 87.6 \\ \hline DT & 91.7 & 0.91 & 0.83 & 90.8 & 89.5 & 90.0 \\ \hline RT & 91.7 & 0.90 & 0.91 & 92.4 & 92.3 & 91.7 \\ \hline \end{tabular}
\end{table} 
Table 2: The results of comparing machine learning algorithms based on various performance metrics using 10-fold cross-validation for the Antifp_DS1 dataset.