## 5 Experimental Results
### Experiment 2: Results Based on the ISIC-2016 Dataset  
The results of all optimizers based on the ISIC-2016 dataset are reported in Table 4. The optimizers selected from 128 features that were extracted using the DL algorithm. The resulting feature sets were used to train the binary classifiers for the difference between the malignant and benign classes. The AHA-AO selected the lowest number of features compared to the other optimizers. PSO, MFO, WOA, AO, AHA, and AHA-AO selected 86 (67.2%), 58 (45.3%), 56 (43.8%), 53 (41.4%), 60 (46.9%), and 52 (40.6%) of the raw features, respectively. Based on the best 52 features, the AHA-AO achieved the best results based on the SVM classifier (i.e., accuracy = 0.8734, BA = 0.7654, F1-score = 0.8683, recall = 0.8734, and precision = 0.8667). In addition, the AHA-AO achieved superior results with the LDA (accuracy = 0.8628, BA = 0.7588, F1-score = 0.859, recall = 0.8628, and precision = 0.8569) and DT (accuracy = 0.8179, BA = 0.7208, F1-score = 0.8193, recall = 0.8179, and precision = 0.8207) compared to the results when using the LDA and DT with other optimizers. The models with the extra FS step achieved better performance than the deep learning models without this step; see Table 3. Because the AHA-AO used the smallest feature set, the time complexity of the model was better than that of the other optimizers for all classifiers (i.e., 0.0398 with DT, 0.0349 with LDA, and 0.1132 with SVM). As noted, the LDA classifier had the shortest time of 0.0349 compared to the times taken by all of the other classifiers. The SVM had the shortest time of 0.1132 compared with all other SVM classifiers with the other optimizers. Note that all of the performance metrics were consistent, so an algorithm with the highest accuracy would have the highest values for the other metrics. DT is a simple classifier. Thus, it was not able to fit a dataset well. As a result, it achieved the lowest performance with all feature selection optimizers. The best-performing DT was based on the AHA optimizer's feature set (BA = 0.8285), and the worst DT was based on the AO feature set (BA = 0.7942). Using the BA as a metric for comparison, DT achieved a performance of 0.6991, 0.6641, 0.6791, 0.6859, 0.7274, and 0.7208 with PSO, MFO, WOA, AO, AHA, and AHA-AO, respectively. The SVM achieved the best results with the PSO feature set (accuracy = 0.8628, BA = 0.7488, F1-score = 0.8573, recall = 0.8628, and precision = 0.8551). However, compared to the DT and LDA, it took the longest time of 0.155. Similarly, the SVM achieved the best results with the MFO optimizer (accuracy = 0.8628, BA = 0.7437, F1-score = 0.8564, recall = 0.8628, and precision = 0.8544). However, it had the longest time of 0.1232. Based on the WOA feature set, the LDA had the best results (accuracy = 0.8602, BA = 0.7571, F1-score = 0.8567, recall = 0.8602, and precision = 0.8546), and it had the shortest time of 0.0379. The LDA achieved the best results with the AO feature set (accuracy = 0.8628, BA = 0.7387, F1-score = 0.8554, recall = 0.8628, and precision = 0.8538), and, again, it had the best time of 0.0373. With the AHA optimizer, the LDA achieved the highest results (accuracy = 0.8681, BA = 0.747, F1-score = 0.861, recall = 0.8681, and precision = 0.8598), and it was the fast classier (i.e., time = 0.038). As a result, the SVM worked