## 8 Conclusions and Future Work  
This paper proposed the use of an MHOANN with a PSO or CSO as the optimization technique and a cost sensitivity fitness function within a majority voting ensemble learning system to handle the imbalanced distribution of financial distress datasets and maximize the prediction of positive instances. Experiments were conducted using datasets of Spanish companies, Taiwanese companies, and Polish companies. Then, we compared the results from the proposed approach to those that were obtained by applying the same MHOANN with a PSO or CSO but using MSE or accuracy as the fitness function.  
The proposed method was able to provide better estimations for the financial distress prediction by avoiding biased results. The results showed that the cost sensitivity fitness function had an extremely positive overall effect on the accurate prediction of the minor classes in imbalanced datasets, with a significant improvement in the g-mean score and a moderately positive impact on the F1 score. Moreover, adapting the majority voting ensemble learning system improved the accuracy and the g-mean scores, along with a significant increase in the F1 scores. One primary limitation of this work was not having access to a domain expert to define the weights for the FN predictions, which is common in cost-sensitive learning [54]. It would be beneficial to obtain domain expert opinions and compared them to the proposed method to find the best weight for the FN instances.  
In the future, we aim to investigate the application of the proposed method to other bankruptcy datasets. Additionally, we aim to use the same proposed approach for other imbalanced classification problems. Moreover, we aim to explore other methods for hyperparameter tuning, including finding the costs of misclassified instances, such as AutoML [55].  
**Author Contributions:** Conceptualization, S.A.-D.S. and H.F.; methodology, S.A.-D.S.; software, S.A.-D.S.; validation, S.A.-D.S., H.F. and P.A.C.; formal analysis, H.F. and P.A.C.; investigation, S.A.-D.S.; resources, S.A.-D.S.; data curation, S.A.-D.S.; writing--original draft preparation, S.A.-D.S.; writing--review and editing, H.F. and P.A.C.; visualization, S.A.-D.S.; supervision, H.F. and P.A.C.; project administration, P.A.C. and H.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Ministerio Espanol de Ciencia e Innovacion, under project number PID2020-115570 GB-C22 (DemocratAI:UGR).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset of Spanish companies was bought from [[http://infotel.es](http://infotel.es)]([http://infotel.es](http://infotel.es)) (accessed on 1 May 2017), the dataset of Taiwanese companies was downloaded from [[https://www.kaggle.com/datasets/fedesoriano/company-bankruptcy-prediction](https://www.kaggle.com/datasets/fedesoriano/company-bankruptcy-prediction)]([https://www.kaggle.com/datasets/fedesoriano/company-bankruptcy-prediction](https://www.kaggle.com/datasets/fedesoriano/company-bankruptcy-prediction)) (accessed on 1 March 2020), and the dataset of Polish companies was downloaded from [[https://www.kaggle.com/competitions/companies-bankruptcy-forecast/data](https://www.kaggle.com/competitions/companies-bankruptcy-forecast/data)]([https://www.kaggle.com/competitions/companies-bankruptcy-forecast/data](https://www.kaggle.com/competitions/companies-bankruptcy-forecast/data)) (accessed on 28 June 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.