# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
## 4 Results
### Summary of Results  
Table 3 presents the best performance of the classifiers with different optimization techniques. Four classification algorithms were implemented with PSO, BGWO, and BGWOPSO and were tested for accuracy and the AUC scores with different training and testing ratios. The results in Table 3 show the best performance of the optimization techniques and the classifiers. It can be observed from Table 2 that BGWOPSO was the best optimizer for all classifiers such as SVM, K-Nearest neighbors, ANN, and Logistic Regression.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ratio** & \multicolumn{2}{c}{**60:40**} & \multicolumn{2}{c}{**70:30**} & \multicolumn{2}{c}{**80:20**} & \multicolumn{2}{c}{**90:10**} \\ \hline
**Optimization** & **Accuracy** & **AUC** & **Accuracy** & **AUC** & **Accuracy** & **AUC** & **Accuracy** & **AUC** \\ \hline \multicolumn{8}{c}{Classifier: SVM} \\ \hline PSO & 89.75 & 91.18 & 89.35 & 90.42 & 88.55 & 89.54 & 87.68 & 91.34 \\ \hline BGWO & 92.96 & 93.45 & 86.64 & 89.23 & 89.96 & 92.34 & 87.44 & 89.88 \\ \hline BGWOPSO & 93.42 & 94.48 & 92.56 & 95.23 & 97.92 & 98.87 & 96.45 & 97.25 \\ \hline \multicolumn{8}{c}{Classifier: K-Nearest Neighbor} \\ \hline PSO & 85.32 & 89.65 & 88.74 & 91.83 & 83.42 & 91.35 & 82.90 & 87.35 \\ \hline BGWO & 88.24 & 93.75 & 90.65 & 92.34 & 89.25 & 94.64 & 92.95 & 99.71 \\ \hline \multicolumn{8}{c}{Classifier: Artificial Neural Network} \\ \hline PSO & 88.52 & 90.11 & 85.27 & 91.84 & 96.24 & 97.24 & 85.36 & 92.88 \\ \hline BGWO & 85.23 & 92.73 & 94.49 & 96.15 & 97.5 & 98.93 & 92.64 & 94.33 \\ \hline BGWOPSO & 92.74 & 93.47 & 95.89 & 98.75 & 99.1 & 1.0 & 93.69 & 96.35 \\ \hline \multicolumn{8}{c}{Classifier: Logistic Regression} \\ \hline PSO & 83.24 & 86.27 & 83.12 & 87.46 & 89.78 & 90.75 & 87.54 & 89.18 \\ \hline BGWO & 86.75 & 88.56 & 88.43 & 90.36 & 90.23 & 91.78 & 88.23 & 90.55 \\ \hline BGWOPSO & 89.74 & 90.57 & 90.17 & 93.12 & 92.55 & 93.36 & 90.14 & 92.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of each classifier with optimization techniques on Instagram dataset.  
The ROC curve is the representation of the trade-off between the True Positive Rate (TPR) and False Positive Rate (FPR). The accuracy of the classifiers can be determined by the curves, those that are closer to the upper left corner perform better. In general, the classifiers generate the ROC curve along the diagonal (FPR = TPR). The closer the curve is to the 45-degree diagonal in the ROC space, the less accurate the test. The accuracy of a classifier is tested by finding the area under the ROC (AUC) curve as it gives the ratio of TPR and FPR. The ROC (Receiver Operating Characteristic) curve for the test data for four classifiers SVM, KNN, Logistic Regression, and ANN is shown in Figures 2-14. The performance of the classifiers with the hybrid optimization method BGWOPSO is better than with BGWO and PSO. Among the classifiers, ANN performed the best, with an AUC of 1.0, which is the highest value. Figures 2-4 show the ROC curve of Logistic Regression with BGWO, PSO, and BGWOPSO; Figures 5-7 show the ROC curve of SVM with BGWO, PSO, and BGWOPSO; Figures 8-10 show the ROC curve of KNN with BGWO, PSO, and BGWOPSO; Figures 11-13 show the ROC curve of ANN with BGWO, PSO, and BGWOPSO; Figure 14 shows a combined ROC curve for all classifiers with  
Figure 4: ROC of Logistic Regression with BGWOPSO.  
Figure 5: ROC of SVM with BGWO.  
Figure 3: ROC of Logistic Regression with PSO.  
Figure 8: ROC of KNN with BGWO.  
Figure 6: ROC of SVM with PSO.  
Figure 7: ROC of SVM with BGWOPSO.  
Figure 11: ROC of ANN with BGWO.  
Figure 10: ROC of KNN with BGWOPSO.  
Figure 9: ROC of KNN with PSO.  
Figure 14: ROC for all classifiers with BGWOPSO.  
Figure 12: ROC of ANN with PSO.  
Figure 13: ROC of ANN with BGWOPSO.