## 4 Experimental Results
### Spam Detection Models and Results
The main reason is that we first increased the dimensionality in our autoencoder model, so many parameters were used. The GRU models had the second-highest training time, but these models take less than an hour to train. Test times were completed in a few minutes. The training and testing times of our MLP models were close to the GRU models and took a relatively shorter time. As a result, although the network-training process of our EGMA model was long due to the large number of parameters and Colab resource transfers, the test times were pretty reasonable. With better resource utilization, the proposed model will achieve fast and successful results in real-world spam detection scenarios. Table 3 presents the success scores of the twelve models applied to the SMS Spam Collection dataset. The table is ordered from the highest accuracy score to the lowest. The highest score, 99.21%, was obtained by the weighted majority voting of the three most successful models, indicated by an asterisk, using ensemble learning. This score is followed by the GRU39 model, with 98.99%. The overall ranking is EGMA > GRU > MLP > AX (Figure 6). In the overall evaluation of the results, it is observed that the performance of the MLP models improves when features are added. On the other hand, in the GRU model, the version without added features showed the highest performance. Adding features had a negative effect on the hybrid model encoded with autoencoder and then classified with XGBoost. In models other than the GRU, the text vectorization algorithms TF-IDF and CountVectorization were tested separately. In the MLP models, the TF-IDF algorithm was observed to improve performance slightly. However, the TF-IDF algorithm significantly decreased the performance in the AX40 model, where autoencoder and XGBoost were used together. This shows that word vectorization algorithms can behave differently depending on the model. A confusion matrix is a basic tool for evaluating the performance of classification models in detail. It visualizes the relationships between the actual classes and the classes predicted by the model, revealing the numerical distribution of correct and incorrect classifications. This approach makes it possible to determine which classes the model succeeds in and which need improvement. Especially in datasets with imbalances between classes, the complexity matrix plays a critical role in better understanding model performance. Due to its unbalanced distribution, the SMS Spam Collection dataset is in this category. Therefore, in this study, the findings of the confusion matrices of the models according to the results obtained on the SMS Spam Collection dataset are presented in Table 3.0

To test the models' stability, the GRU39, TFMLP59, and AX40 models, which performed best on the SMS Spam Collection dataset, were applied to three additional datasets. The results obtained were combined using the EGMA method. The results of the datasets used are presented in Table 4 in comparison with precision, recall, F1-score, and accu

Table 3.1 Bar chart of the success of the models. racy metrics. Although the proposed models achieved successful results individually, the ensemble learning approach achieved the highest classification success. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multicolumn{5}{l}{(a) The SMS Spam Collection} \\ \hline GRU39 & 0.99 & 0.94 & 0.96 & 0.9899 \\ TFMLP59 & 0.98 & 0.94 & 0.96 & 0.9899 \\ AX40 & 0.99 & 0.91 & 0.95 & 0.9871 \\ EGMA & 1.00 & 0.94 & 0.97 & 0.9928 \\ \hline \multicolumn{5}{l}{(b) Email Spam} \\ \hline GRU39 & 0.98 & 0.99 & 0.98 & 0.9890 \\ TFMLP59 & 0.98 & 1.00 & 0.99 & 0.9924 \\ AX40 & 0.90 & 0.89 & 0.90 & 0.9275 \\ EGMA & 0.98 & 1.00 & 0.99 & 0.9924 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Success scores of our models on spam datasets. Figure 7: Confusion matrices of the models.