## 4 Experimental Results
### Spam Detection Models and Results
While the weighted and equal majority method did not show significant differences in the SMS Spam Collection dataset, the weighted majority method achieved better results on the other three datasets. Therefore, the EGMA method was improved on these three datasets, to achieve a higher classification performance. To better evaluate the study's success, the literature methods were compared. Table 5 presents the techniques used in this study and similar studies in the literature and their success scores. As can be seen from the results, the proposed EGMA model achieved the best results on all datasets. The results show that the proposed model is highly robust for spam detection on balanced and unbalanced spam datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline (c) Enron-Spam & & & & \\ \hline GRU39 & 0.98 & 0.99 & 0.99 & 0.9857 \\ TFMLP59 & 0.98 & 0.99 & 0.99 & 0.9875 \\ AX40 & 0.96 & 0.97 & 0.97 & 0.9663 \\ EGMA & 0.99 & 0.99 & 0.99 & 0.9900 \\ \hline (d) Super SMS & & & & \\ \hline GRU39 & 0.99 & 0.98 & 0.98 & 0.9870 \\ TFMLP59 & 0.98 & 0.98 & 0.98 & 0.9841 \\ AX40 & 0.97 & 0.93 & 0.95 & 0.9620 \\ EGMA & 0.99 & 0.98 & 0.98 & 0.9871 \\ \hline (e) UtkMI’s Twitter Spam & & & & \\ \hline GRU39 & 0.94 & 0.97 & 0.95 & 0.9509 \\ TFMLP59 & 0.93 & 0.93 & 0.92 & 0.9215 \\ AX40 & 0.85 & 0.87 & 0.86 & 0.8546 \\ EGMA & 0.94 & 0.97 & 0.95 & 0.9509 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Study** & **The Method** & **Accuracy** \\ \hline \multirow{6}{*}{SMS Spam Collection} & [33] & BERT & 0.9901 \\  & [39] & Spam Transformer & 0.9892 \\  & [54] & Logistic Regression & 0.9841 \\  & [38] & DCF & 0.9838 \\  & [55] & KNN & 0.8970 \\  & **Our study** & **EGMA** & **0.9928** \\ \hline \multirow{6}{*}{Email Spam} & [56] & CNN & 0.9902 \\  & [57] & Classical Machine Learning and LSTM & 0.9823 \\  & [58] & BERT, KNN, LSTM, and Bi-LSTM & 0.9794 \\  & **Our study** & **EGMA** & **0.9924** \\ \hline \multirow{6}{*}{Enron-Spam} & [59] & MLP and CML & 0.9876 \\  & [60] & Innovative TWRM method & 0.9500 \\ \cline{1-1}  & [61] & CML (ID3 with Hidden Markov Model) & 0.8900 \\ \cline{1-1}  & **Our study** & **EGMA** & **0.9900** \\ \hline \multirow{6}{*}{Super SMS} & [62] & Deep ML with Ensemble Learning & 0.9840 \\  & [48] & TCSVM & 0.9780 \\ \cline{1-1}  & [62] & CML & 0.9550 \\ \cline{1-1}  & **Our study** & **EGMA** & **0.9871** \\ \hline \multirow{6}{*}{UtkMI’s Twitter Spam} & [37] & ELMo & 0.9000 \\ \cline{1-1}  & [40] & TextSpamDetector & 0.8835 \\ \cline{1-1}  & [39] & Spam Transformer & 0.8706 \\ \cline{1-1}  & **Our study** & EGMA & 0.9509 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with results in the literature. The models we created in the study were also tested with a dataset consisting of Twitter messages (UtkMI's Twitter Spam). When the results we obtained are analyzed, it is seen that higher success is achieved than the studies using the same datasets in the literature. In similar studies using ensemble learning algorithms, lower accuracy values are obtained with the same datasets [36; 38]. Unlike similar ensemble learning models in the literature, the EGMA model we created in our study allows us to create a more robust model using models with different approaches such as a GRU, hybrid autoencoder, and MLP. ## 5 Conclusions

In this study, we developed the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, combining the strengths of gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models through a majority voting algorithm. The proposed model was rigorously tested on five datasets--SMS Spam Collection, Email Spam, Enron-Spam, Super SMS, and UtkMI's Twitter Spam--to evaluate its effectiveness across various spam detection scenarios.