## 4 Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Data**} & \multirow{3}{*}{\(\beta\)} & \multicolumn{6}{c}{**Validation**} \\ \cline{3-10}  & & \multicolumn{3}{c}{**Classification**} & \multicolumn{3}{c}{**Regression**} \\ \cline{3-10}  & & **Accuracy** & **Precision** & **Recall** & **F1-Measure** & **MSE** & **RMSE** & **MAE** \\ \hline \multirow{3}{*}{No resampling} & \(\beta=0.3\) & 0.96 & 0.72 & 0.77 & 0.74 & 0.390 & 0.625 & 0.276 \\ \cline{2-10}  & \(\beta=0.4\) & 0.94 & 0.64 & 0.75 & 0.67 & 0.416 & 0.645 & 0.304 \\ \hline \multirow{3}{*}{SMOTE} & \(\beta=0.4\) & 0.98 & 1.00 & 0.96 & 0.98 & 0.547 & 0.739 & 0.335 \\ \cline{2-10}  & \(\beta=0.5\) & 0.96 & 0.72 & 0.76 & 0.74 & 0.457 & 0.689 & 0.289 \\ \hline \multirow{3}{*}{SMOTE + RU} & \(\beta=0.3\) & 0.99 & 0.99 & 0.99 & 0.99 & 0.528 & 1.727 & 0.308 \\ \cline{2-10}  & \(\beta=0.4\) & 0.97 & 0.99 & 0.95 & 0.97 & 0.547 & 0.739 & 0.335 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results obtained using the proposed model using different values of \(\beta\). when using a batch size of 256 and a learning rate of \(1\times 10^{-3}\). Additionally, as it can be seen, the proposed model yielded better performance for a batch size of 256 when applying hybrid SMOTE along with RU. One should note that while validating the model, the ReduceLROnPlateau method was used to reduce the learning rate when the loss function stops improving. The parameter 'Patience' was set to 15. This means that the learning rate is reduced if no improvement is recorded for 15 epochs and min_lr to \(1\times 10^{-7}\). Moreover, to prevent overfitting, 'EarlyStopping' was used and 'patience' was set to 100. In other words, the training stops when the validation loss does not improve for 100 epochs. One should note that, for \(N_{t}\) training instances and a batch size of \(\text{b}_{\text{s}}\), we need \(N_{t}/\text{b}_{\text{s}}\) iterations to complete one epoch. To further tune the performance of the proposed model after applying SMOTE and hybrid SMOTE with RU to the entire dataset, we investigated the model with different values of \(\beta\) and recorded the attained performance as depicted in Table 6. As such, the proposed model achieved better regression performance when resampling the dataset. In particular, associating 15% resampling with SMOTE and RU yielded the best classification and regression performance. The classification performance, which was initially good, did not improve further. This proves that the additional data improved the generalization of the regression model without affecting the classification capability of the model. The experiment results reported above show that training the proposed model on a larger and more balanced dataset improves the regression's performance. In other words, data resampling improved model generalization. As the first contribution of this research consists of classifying Zakat declarations into the \"under-reporting\" and \"actual declaration\" classes, recall is more important than precision for the classification task. On the other hand, for the prioritization of auditing achieved through the regression task, the revenue at risk represents the main performance metric for this supervised learning task. Table 6 reports the results obtained using the best proposed model. Namely, the ROC curve in Figure 3a and the expected revenue at risk vs. the auditing rate in Figure 3b were achieved using the proposed model, SMOTE + RU and a sampling rate of 15%. Note that the learning rate, the batch size and \(\beta\) were set to \(1\times 10^{-3}\), 256 and 0.3, respectively. As it can be seen, auditing 40% of positive cases yields 79% of the expected revenue. In other words, the proposed system requires a 40% auditing coverage to collect 79% of Zakat revenue. This proves that a tradeoff between the performance of the classifier and the prioritization of auditing tasks has been successfully established.