## 5 Experimental Results
### Experiment 3: Results Based on the PH2 Dataset  
The results of all optimizers based on the ISIC-2016 dataset are reported in Table 5. In this experiment, the feature selection optimizers selected from 512 features that were learned by the DL module. The feature optimization techniques selected different numbers of features (i.e., 107 (20.9%), 141 (27.5%), 159 (31.1%), 221 (43.2%), 222 (43.4%), and 326 (63.7%) with AHA-AO, AHA, AO, WOA, MFO, and PSO, respectively). The selected feature sets were used to train classifiers for a three-class classification problem to separate the common nevus, atypical nevus, and melanoma classes. Even though this was a more complex classification task than that in Experiment 1, most classifiers achieved better results in Experiment 2 than in Experiment 1. As can be clearly noticed in Table 5, the proposed AHA-AO selected the smallest feature set (107 features), and it achieved the best results with the SVM classifier (accuracy = 0.975, BA = 0.9792, F1-score = 0.975, recall = 0.975, and precision = 0.975) compared to the results of other classifiers tuned with all other optimizers. The resulting models with the extra FS step achieved better performance than the deep learning models without this step; see Table 3. We noticed a huge difference between the AHA-AO and the other optimizers regarding the time. All classifiers took less time compared to their corresponding classifiers with other optimizers. In other words, the AHA-AO-based SVM had the shortest time (0.0523) compared with all other SVM classifiers. The AHA-AO-based LDA had the shortest time (0.0937) compared with all other LDA algorithms, and the DT had the shortest time (0.0482) compared to all other classifiers. The DT showed high results compared with those of other DT algorithms (accuracy = 0.9107, BA = 0.9048, F1-score = 0.9115, recall = 0.9107, and precision = 0.9192); in addition, it achieved the lowest time complexity (0.0482). Next to the AHA-AO, the AHA selected 141 features, and AO selected 159 features. The AHA achieved the highest results when using the LDA classifier (accuracy = 0.9643, BA = 0.9702, F1-score = 0.9643, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Alg.** & **Model** & **Accuracy** & **BA** & **Fl-Score** & **Recall** & **Precision** & **Time** & **No. of Features** \\ \hline \multirow{4}{*}{PSO} & DT & 0.8153 & 0.6991 & 0.8134 & 0.8153 & 0.8117 & 0.0449 & \\  & LDA & 0.8549 & 0.7438 & 0.8504 & 0.8549 & 0.8479 & 0.0463 & \\  & SVM & 0.8628 & 0.7488 & 0.8573 & 0.8628 & 0.8551 & 0.155 & 86 \\ \hline \multirow{4}{*}{MFO} & DT & 0.7995 & 0.6641 & 0.7951 & 0.7995 & 0.7916 & 0.044 & \\  & LDA & 0.8575 & 0.7455 & 0.8527 & 0.8575 & 0.8503 & 0.0511 & \\  & SVM & 0.8628 & 0.7437 & 0.8564 & 0.8628 & 0.8544 & 0.1232 & 58 \\ \hline \multirow{4}{*}{WOA} & DT & 0.8074 & 0.6791 & 0.8038 & 0.8074 & 0.8008 & 0.0395 & \\  & LDA & 0.8602 & 0.7571 & 0.8567 & 0.8602 & 0.8546 & 0.0379 & \\  & SVM & 0.8549 & 0.7438 & 0.8504 & 0.8549 & 0.8479 & 0.148 & \\ \hline \multirow{4}{*}{AO} & DT & 0.7942 & 0.6859 & 0.7962 & 0.7942 & 0.7984 & 0.039 & \\  & LDA & 0.8628 & 0.7387 & 0.8554 & 0.8628 & 0.8538 & 0.0373 & \\  & SVM & 0.8575 & 0.7304 & 0.8499 & 0.8575 & 0.8478 & 0.1196 & 53 \\ \hline \multirow{4}{*}{AHA} & DT & 0.8285 & 0.7274 & 0.8281 & 0.8285 & 0.8276 & 0.0492 & \\  & LDA & 0.8681 & 0.747 & 0.861 & 0.8681 & 0.8598 & 0.038 & \\  & SVM & 0.8628 & 0.7337 & 0.8544 & 0.8628 & 0.8533 & 0.1225 & 60 \\ \hline \multirow{4}{*}{AHA-AO} & DT & 0.8179 & 0.7208 & 0.8193 & 0.8179 & 0.8207 & 0.0398 & \\  & LDA & 0.8628 & 0.7588 & 0.859 & 0.8628 & 0.8569 & **0.0349** & \\ \cline{1-1}  & SVM & **0.8734** & **0.7654** & **0.8683** & **0.8734** & **0.8667** & 0.1132 & **52** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results of each feature selection optimization algorithm on the ISIC-2016 dataset (bold refers to best value). recall = 0.9643, and precision = 0.9643), but this classifier took a longer time (0.1095) than DT and SVM did.