## 3 Materials and Methods
### Data Characteristics Influencing Algorithm Performance  
Classification from imbalanced data is a complex problem since it is affected by many factors such as the ratio of imbalance, the overlap between the majority and minority class, borderline examples and small sample size, which causes low performance of the classification models. Detecting these factors in the skewed data and discovering their influence on the classification performance is a research challenge of great interest due to its presence in many areas.  
The imbalance ratio (IR) or imbalanced class distribution is defined as the ratio of the number of examples in majority class to the number of examples of the minority class [73]. The imbalance ratio takes place in many cases in real-world datasets and many application domains. Low imbalance ratio and balanced distribution between the classes always gives a better result in the performance of the models. There are different opinions about the effect of class distribution (imbalance ratio), but most of researchers have confirmed that the imbalance ratio between the majority and the minority class is not a problem in itself because there is a relationship between imbalance ratio, borderline examples and overlapping of classes.  
Overlapping between classes is an important factor that impacts the performance of the model, especially given the imbalanced distribution of classes. Overlapping means that the minority class instances have properties that are similar to the majority class instances. The overlapping problem makes it difficult for classifiers to identify the limits of a good decision between classes, resulting in decreased performance. The presence of the overlap problem in data with imbalanced distributions becomes increasingly complex because of the need for strategies to balance the data. The application of these strategies, such as oversampling methods, involves the replication of the number of instances in the minority class. This leads to increasing similarity between instances, especially when the imbalance ratio is high and high replication rate is required.  
The problem of borderline examples also impacts on the reduction of the classifier efficiency [74]. It means that there are examples in the area around the separation boundaries, where the minority and majority class overlap [75]. The problem of borderline examples is related to noise data. The percentage of borderline examples increases as the number of instances increases because the greater number of instances in the dataset the more noise. This may happen when applying oversampling techniques, such as ROS or SMOTE, because these strategies replicate data, especially in high-imbalance distributions (high imbalance ratio).  
Another problem that arises in classification is the small sample size of the training dataset. This factor is related to information lack or density lack. In this case, the algorithm does not have enough information to construct and validate the model. The situation becomes more complicated with imbalance distributions. One of the reasons that cause a small sampling problem in data is the application of random undersampling (RUS) to balance the data, especially when the volume of data is low with a high imbalance ratio, in this case a high percentage of sampling is lost. In addition, the performance of the model is affected when the dataset has a small size and at the same time the number of instances in the minority class is very low. Such a scenario makes the classifier is biased to the majority class; thus, it fails to properly classify the minority class instances and lead to performance loss.