## 2 Related Works
The authors in [36] have suggested a novel ensemble approach, \"Dynamic Ensemble Selection for Multi-class Imbalanced the dataset (DES-MI)\" to handle the multi-class problem's challenge. To improve learning from multi-class problems, the DES-MI model first creates a balanced training set before choosing an appropriate classifier. To compare the accuracy and computational cost of the well-known boosting-based ensemble classifier \(X\)gboost (ensemble-based method), the authors in [37] looked at its general performance, efficiency, competence, and effectiveness while taking into account its sensitivity to the sample size and feature space. The performance of statistical analysis, according to the authors, is greatly improved when parameterizing \(X\)gboost using a Bayesian approach as opposed to utilizing \"random forests\" and \"support vector machines\" that are operated on larger sample size. The authors in [38] described the suitability of data preprocessing techniques to address the data imbalance issues. The authors of this study are of the opinion that a balanced training dataset is more robust for improving the overall performance of the classifier for several base classifiers. [PERSON] and [PERSON] in [39] proposed a new technique by achieving undersampling through the kNN classifier. Based on the data features of the data distribution, four undersampling methods based on kNN are proposed, namely \(Near_{Miss1},Near_{Miss2},Near_{Miss3}\) and the \"most distant\" method, in which a small subset of training data is selected to minimize the skewness in the reaming data. The authors in [40] highlighted that sampling techniques are very clever in dealing with the binary classification problems with two target variables, facing some difficulties when directly applied to solve multi-class classification problems. The authors used the \"Mahalanobis Distance-based Over-sampling (MDO)\" [41] technique to handle the imbalanced class data with a mixed attribute, introduce generalized singular value decomposition (GSVD) for complex and mixed-type data, augmenting with a resampling scheme applied on the mixed type of attributes to optimize the synthesis of samples. In [42], the authors presented a combination of k-means clustering and \"(SMOTE)\", which results in an effective \"oversampling method\" and effectively overcomes the imbalance ratio between and within classes and avoids noise generation. K-means clustering is a three-step method: clustering, filtering,and sampling to enhance learning from multi-class classification problems. To address the imbalance classification problems, the authors combine \"random oversampling\" and \"random under sampling techniques\" in [43] to propose a hybrid sampling SVM approach. By using the undersampling technique, the samples with the least significance were deleted, followed by the oversampling technique to generate some samples in the minority class. In [44], the authors first proposed an optimization classification model (OCM) to deal with the classification problems using evolutionary computation (EC) techniques. In the second step, the authors proposed a novel algorithm, the self-adaptive fireworks algorithm (SaFWA) based on swarm intelligence, to address the optimization problems. To increase the diversity/range of solutions, four candidate solution generation strategies (CSGSs) were merged with SaFWA. In [45], the authors highlighted the multi-class problem solution strategies (decomposition strategies), i.e., transforming imbalance multi-class problems into several classes and designing a separate classifier for each class (binary decomposition) is considered to be the most prominent approach for multi-class decomposition). The proposed model has the flexibility to discard the non-competent classifier to improve the robustness of the combination phase.