## 2 Related Works  
The authors in [25] argued for the significance of ensemble approaches as compared to the sampling methods and individual classifiers when applied to a multi-class imbalanced dataset. The authors compared the ensemble-based approach with the non-ensemble to prove the robustness of ensemble methods. [PERSON] and [PERSON] in [26] combined AdaBoost.NC and AdaBoost with sampling techniques either augmented with or without the decomposition techniques to address the multi-class imbalance problem and highlight that the ensemble approach is more effective than the oversampling and decomposition techniques. Without class decomposition, AdaBoost.NC shows better performance as compared to AdaBoost, but with the increasing number of classes, their performance also decreased gradually. The authors in [26] showed that the shortcomings of sampling methods cannot be avoided by using standard ensemble approaches if the dataset has multiple classes. Despite the increasing number of samples in the positive class by adding some samples through oversampling, the distribution of classes in the data space is still imbalanced, which is dominated by the majority class. [PERSON] et al. [27] proposed SMOTEBoost, an ensemble-based sampling technique to enhance the conventional SMOTE [28] by mingling it with AdaBoost.M2. The authors applied SMOTE before the base classifier evaluation, and thus, the new instance's weight is relative to the numbers of samples in the new dataset. After producing the new instances, the original instance's weight is standardized to generate the new distribution. In every iteration, the instance weight of the minority class increases. The authors in [29] highlighted the outclass performance of bagging as compared to boosting in a multi-class and noisy environment. Furthermore, bagging techniques have to be quickly developed and become more powerful if properly ensemble. Similarly,OverBagging [30] combines the data preprocessing and bagging techniques to manage the class imbalance issue by increasing the positive class cardinality by the duplication of original examples; at the same time, the instance in the majority of negative class is considered in every bag to increase multiplicity. In [31], SMOTEBagging was proposed to counter multi-class imbalance learning issues by creating every individual bag to be expressively diverse. In every iteration during bag creation, the SMOTE resampling rate is defined and this ratio specifies the positive class instances randomly resampled from the original dataset, and the remaining positive class instances are generated by SMOTE. [PERSON] et al. proposed UnderBagging for the first time in [31], wherein the negative class instances are arbitrarily condensed at every bootstrap sample to make it equal to the cardinality of the positive class. The basic, simple version of undersampling when merged with bagging-based techniques proves that it is more significant than the more composite solution, such as BalanceCascade [32] and EasyEnsemble [33]. In [34], the authors highlighted an important problem with the ensemble size and ensemble cardinality (number of component classifiers in the final ensemble), as it affects the predictive performance, time, and memory for the classification algorithm when applied to imbalance data and diversity among the component classifier. The boosting ensemble method presented in [35] is one of the prominent methods explicitly based on the complementarity among the component classifier. Through the boosting process, a strong learner built from the collection of different weak learners (weak in the sense of accuracy while being applied on the classification task). In [35], the authors proposed a new ensemble method, twin bounded weighted relaxed support vector machines (TBWSVM), which is an extension of the weighted relaxed support vector machine (WRSVM) classifier used for class imbalance problems and outliers. The resulting classifier utilizes twin bounded support vector machines (TBSVM), which provides a quick classification method.