## 4 Resampling Techniques  
There are various techniques for dealing with the imbalanced dataset [20]. These techniques are summarized and categorized into four major groups: the algorithm level, data level, cost-sensitive learning group, and ensemble-based group. First, the algorithm level technique aims at making an attempt to adapt current classifier learning algorithms to bias the learning for the minority class. Second, the purpose of the data level technique is to rebalance the classification distribution through resampling data space. Next, the cost-sensitive learning technique combines the algorithm and data level techniques to optimize the total cost errors to both classes. Finally, the ensemble-based approaches include a combination between one of the aforementioned techniques and an ensemble learning algorithm, such as the data level and cost-sensitive techniques. Moreover, the data level methods are separated into oversampling methods, undersampling methods, and hybrid methods. The under-sampling approaches drop instances of the majority class to make a subset of the primitive dataset. This leads to the loss of much valuable information on the original dataset. Conversely, the oversampling methods randomly duplicate instances in the minority class, so that the dataset size may increase,  
Figure 3: Plot Amount value. and this results in a rising training time for the ML models. The hybrid approach mixes both sampling methods, but these methods are quite complex. In order to address these disadvantages, we use the SMOTE and ADASYN techniques to cope with the imbalanced dataset. This is because both techniques are popular and have demonstrated their benefits when solving imbalanced datasets in many different applications [21; 22; 23]. The SMOTE is one of the popular and effective oversampling techniques. The SMOTE is used to duplicate existing instances of the minority class and permitted to make a new artificial instances class based on knowledge about the neighbors that surround each sample in the minority class. The SMOTE is separated into three parts, which are known as randomizing the minority class instances, calculating k nearest neighbors for each minority class instance, and producing synthetic instances [24]. According to Figure 4[25], this is achieved through linear interpolation of a randomly chosen minority observation and one of its observation's neighboring minorities. To be more specific, this technique has three steps for producing a synthetic sample. The first step is to select a random minority observation \(\overset{\rightarrow}{a}\). The second step is to select instance \(\overset{\rightarrow}{b}\) in its k nearest minority class neighbors. The last step is to create a new sample \(\overset{\rightarrow}{x}\) through random interpolation of the two samples: \(\overset{\rightarrow}{x}=\overset{\rightarrow}{a}+w\times(\overset{ \rightarrow}{b}-\overset{\rightarrow}{a})\), where w is denoted as a random weight in [0,1]. The ADASYN is a technique of improvement from the SMOTE. This technique relies on the idea of utilizing the weighted distribution to adaptively generate minority class samples: more synthetic data is produced for the minority class samples that are tougher to learn compared to the minority class samples, which are more straightforward to learn. The ADASYN technique proved its effectiveness through improving learning relevant to the distributions of data in two ways. This technique minimizes bias caused by a classification imbalance and adapts to the classification decision boundary for difficult samples [26]. The ADASYN, which illustrates input, output, and procedure, is shown in Algorithm 1. Figure 4: SMOTE linear interpolation of a random choose minority sample (k = 4 neighbors). ```
Input: Training dataset Output: Synthetic dataset from the minority class dataset Procedure:
1. The degree of balance \(D_{c}\) is calculated as shown in (2).