## 2 Related Works  
In the literature, much research has been conducted on examining the problem of imbalanced datasets using a variety of methods and approaches in different combinations. For example, a modified version of a support vector machine (SVM) that was based on density weight was proposed in [18] to tackle the binary class imbalance classification problem. Experimental analyses were performed on certain intriguing imbalanced artificial and real-world datasets and their performances were measured using the metrics of the area under the curve and the geometric mean. The results were compared to those from an SVM, a least squares SVM, a fuzzy SVM, an improved fuzzy least squares SVM, a fuzzy SVM that was based on affinity and class probability, and an entropy-based fuzzy least squares SVM. The similar or better generalization results indicated the efficacy and applicability of the proposed algorithms. Deep learning (DL) methods have also been considered to overcome the class imbalance challenge. In [19], the authors presented a novel comparison between three different DL methods: a deep belief network (DBN), long-short term memory (LSTM), and a multilayer perceptron model (MLP). They also compared five ensemble classifiers financial distress prediction: XGBoost, SVM, K-nearest neighbor (KNN), and AdaBoost. A new selective oversampling approach (SOA) that uses an outlier identification technique to separate the most representative samples from the minority classes and then uses these samples for synthetic oversampling was proposed in [20]. Their experiments demonstrated that the suggested method outperformed two state-of-the-art oversampling strategies: synthetic minority oversampling and adaptive synthetic sampling. Moreover, using cost-sensitive learning to solve the imbalanced classification problem has also been very popular in the literature. Robust cost-sensitive classifiers have been constructed by changing the objective functions of well-known algorithms, including logistic regression, decision trees, extreme gradient boosting, and random forests, which can then be then utilized to predict medical diagnoses effectively, as proposed in [21]. Furthermore, the cost-sensitive approaches outperformed the standard algorithms, according to the findings of those experiments. In another study, the authors used decision trees as a boosting method to improve business failure prediction performance. A weighted objective function, weighted cross-entropy, was incorporated into the boosted tree architecture to overcome the class imbalance issue in the business failure datasets, making the weighted XGBoost a cost-sensitive business failure prediction model [22]. Furthermore, using evolutionary algorithms to train artificial neural networks (ANNs) has been very popular since the 1980s. The use of the genetic algorithm (GA) to train an ANN for image classification was discussed in [23]. Additionally, using metaheuristic algorithms to train ANNs to manage the disadvantages of gradient-based methods, particularly backpropagation techniques, has also been extensively researched. During the early 2000s, numerous studies focused on the use of metaheuristic algorithms in neural network training for binary classification tasks, such as financial distress prediction. Metaheuristic approaches were proven to perform better than gradient-based algorithms in [24]. The effects of fitness functions on MHOANN learning when dealing with imbalanced datasets was also discussed in [25]. A PSO algorithm was used to optimize the weights and biases in a neural network architecture to predict bankruptcy among Indian firms in [26]. An artificial neural network that was trained by a metaheuristic artificial bee colony (ABC) algorithm was proposed in [27]. The model was used for corporate bankruptcy prediction and then the proposed method was compared to the multiple discriminant analysis (MDA) model and an ANN that was trained by the most common learning algorithm (backpropagation (BPNN)).