## 6 Conclusions and Future Work  
Compared to many other languages, such as English, there is less experimental study on multilabel Arabic text classification. In recent years, it has been shown that integrating the results of many models helps lower generalization mistakes and handle the large variety of individual classifiers. Additionally, the ensemble might be used to choose the best features in collaboration with the genetic algorithm. As a result, the ensemble is a sophisticated method for coping with the large variety of individual classifiers while reducing overall mistakes and selecting the best features. The concept of creating a prediction model by merging many models into an ensemble. In this study, a framework for multilabel text classification is proposed. The proposed model makes use of a new representation model and ensemble feature selection via metaheuristics using a genetic algorithm. In addition, we implement a unique ensemble classification model. The type of representation, feature selection, and classification algorithm play important roles in enhancing performance. Based on our results, the proposed model is an effective technique to enhance the performance of different scenarios, specifically the multilabel model.  
The findings of the study state that the multilabel classification can be challenging for any language, including the Arabic language. However, Arabic text presents some unique issues, which give more challenges and opportunities for future work. Some of these issues are morphological complexity and various dialects; consequently, designing a model to address these challenges with a multilabel scenario is still interesting. In addition, there are different challenges, such as a lack of large-size balanced labeled datasets and Arabic multilabel pre-trained models. Overall, Arabic multilabel text classification is a rapidly evolving field, and there are numerous opportunities for future work to improve the performance, efficiency, and applicability of these models. Further, we plan to address the limitations of minor classes, label uncertainty, and reduce the number of labeled texts needed to train the models by using models' hyper-parameters optimization, active learning, and zero-shot classification in future work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author and Ref.** & **Year** & **Model** & **Multilabel** & **No. of Topics** & **F1-Score (\%)** \\ \hline [PERSON] et al. [30] & 2022 & Single Model & No & & 61.90 \\ This work & 2023 & Single Model & Yes & 3 & 65.62 \\  & Ensemble Model & Yes & & 68.76 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Comparison with current related work on the new dataset.  
**Author Contributions:** Conceptualization, S.M.A. and A.Y.M.; methodology, S.M.A. and A.Y.M.; software, S.M.A., A.G., M.A.H. and A.Y.M.; validation, S.M.A., A.G., M.A.H. and A.Y.M.; formal analysis, S.M.A., A.G., M.A.H. and A.Y.M.; investigation, S.M.A., A.G., M.A.H. and A.Y.M.; resources, S.M.A., A.G., M.A.H. and A.Y.M.; data curation, S.M.A., A.G., M.A.H. and A.Y.M.; writing--original draft preparation, S.M.A., A.G., M.A.H. and A.Y.M.; writing--review and editing, S.M.A., A.G. and A.Y.M.; visualization, S.M.A., A.G. and A.Y.M.; supervision S.M.A., A.G. and A.Y.M.; project administration, S.M.A. and A.G.; funding acquisition, S.M.A. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset used in this study is available in reference [30].  
**Acknowledgments:** The authors extend their appreciation to the Deputyship for Research & Innovation Ministry of Education in Saudi Arabia for funding this research work through the project number (IF2/PSAU/2022/01/23208).  
**Conflicts of Interest:** The authors declare no conflict of interest.