## 2 Literature Review
### Oversampling Method for Imbalance Dataset  
Classification datasets usually have great differences of distribution between the quantities of majority class and the minority class, which is alluded as an imbalanced dataset. Learning from imbalanced datasets is a demanding problem in supervised learning as standard classification algorithms are intended to explain balanced class distributions. One of the methods is called oversampling, and it works by creating artificial data to attain a balanced class distribution. SMOTE is a kind of oversampling technique that has appeared to be great and it is generally utilized as a part of machine learning to balance imbalanced data. The SMOTE creates arbitrarily new instances of minority class from the closest neighbors of the minority class sample. These instances are made in view of the features of the original dataset with the goal that they end up like the original instances of the minority class [53]. Regarding the implementation of oversampling method, several studies have been conducted and have showed significant results. The SMOTE has been integrated with classification algorithms and it has improved the performance of prediction systems, such as in network intrusion detection, bankruptcy prediction, credit scoring, and medical diagnosis. [PERSON] et al. proposed Region Adaptive Synthetic Minority Oversampling Technique (RA-SMOTE) and applied it to intrusion detection to recognize the attack behaviours in the network [29]. Three distinct sorts of classifiers, including SVM, BP neural system (BPNN), and RFs, were utilized to test the capability of the algorithm. The findings demonstrated that the proposed algorithm could successfully take care of the class imbalance issue and enhance the detection rate of low-visit attacks. [PERSON] et al. proposed a hybrid model by utilizing SMOTE for imbalanced dataset to be used as a tool for bank to evaluate the enterprise credit [30]. The proposed model was applied to the financial data of 552 Chinese listed companies and outperformed the traditional models. [PERSON] et al. used numerous oversampling methods to manage imbalance problems on the financial related dataset that was gathered from Korean organizations between 2016 and 2017 [31]. The findings showed a blend of SMOTE and Edited Nearest Neighbor (SMOTE + ENN), as well as RF achieved highest accuracy on bankruptcy prediction. Finally, a past study proposed a method combining SMOTE with SVM to enhance the predication accuracy for old banknotes [32]. The findings revealed that the proposed method could enhance the performance by as much as 20% when compared with standard SVM algorithm. Generally, greater prediction performance can be attained with balanced data. Past study integrated the SMOTE, the particle swarm optimization (PSO), and radial basis function (RBF) classifier [33]. The experimental results showed that the SMOTE + PSO-RBF provides an extremely well-defined explanation for other present advanced techniques for fighting imbalanced problems. [PERSON] et al. utilized data-cleaning before and after applying SMOTE by proposing selection techniques that are based on fuzzy rough set theory to remove the noisy instances from the dataset [34]. The results indicated that their proposed technique upgrades present pre-processing methods for imbalanced classification. Past study proposed the SMOTE-IFF (Iterative-Partitioning Filter), which can tackle the issues that are created by noisy and borderline cases in imbalanced datasets [35]. The findings revealed that the proposed model worked superior than the present SMOTE. Finally, [PERSON] et al. presented an impressive oversampling method based on k-means clustering and SMOTE, which can prevent the creation of noise and successfully beats imbalances between and within classes [36]. The result showed that their proposed method was applied to 90 datasets and improved the performance of classification.