# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 2 Background and Related Work  
Machine learning algorithms in classification tasks work with the assumption that the distribution of the data with respect to the targeted class variable is equal. However, most classification problems suffer from an imbalanced dataset. Therefore, dealing with imbalanced datasets is considered one of the most important activities in classification tasks. In order to deal with this problem, several solutions have been proposed in the literature [7; 8; 9]. The data-preprocessing-level solutions deal with imbalanced datasets by resampling of data [9; 10]. Resampling is performed by OS minority cases or US majority cases, or by combining US and OS strategies. Using resampling techniques, we can balance the dataset at any desired level of imbalance ratio (IR). It is not necessary that the number of majority and minority cases is exactly the same. Resampling techniques are broadly divided into three categories, namely US, OS, and HS [9]. Under Sampling (US): In this method, the dataset is balanced by deleting majority class instances [10; 11]. The instances are selected randomly and deleted from the dataset until the dataset is balanced. The weakness of this method is that we might lose some potentially useful information required for the learning process when we remove the instances from the majority class data. Over Sampling (OS): In this method, the dataset is balanced by randomly OS minority class instances [12]. This method suffers from duplication of information due to OS of the minority class instances, which might lead to overfitting the model. However, this method does not lose any important information, unlike the RUS method. Hybrid Sampling (HS): In this method, the dataset is balanced by combining the OS and US approaches [13; 14]. Random Over Sampling Examples (RSE): This method is based on smoothed bootstrap-based techniques [15]. Synthetic minority over sampling technique (SMOTE): This method is an OS technique. In this method, instead of replicating minority class instances, new instances are generated synthetically [16]. It follows the following process to generate synthetic data: first, it randomly selects a minority class instance then finds its k-nearest neighbors. Then new instances are generated synthetically by interpolation between the selected minority class instance and its nearest neighbors. There are many literatures that talk about the other variants of SMOTE method such as SMOTEBoost [17], MSMOTE [18], and MWMOTE [19]. One-sided Selection Method (OSS): This method falls under the US techniques category. In this method, borderline and redundant majority class instances are removed [12]. Clustering-Based Under Sampling (CBUS): This method is a US strategy. In this method, US is achieved by creating clusters of majority class instances [20]. The number of clusters should be equal to the number of minority class instances. There are two clustering strategies. In the first strategy, the cluster centre represents the cluster, and in the second strategy, the nearest neighbor of the cluster center represents the cluster. Clustering-Based Over Sampling and Under Sampling (CBOUS): This method is an extension of the CBUS. In this method, data balancing is achieved by combining US and OS approaches by creating clusters of majority and minority class instances [21]. The cost-sensitive methods are based on the assumption that the cost of misclassification of the minority class instances is higher than the cost of misclassification of the majority class instances. Cost-sensitive learning can be incorporated at the data-preprocessing level or at the algorithm level. Cost-sensitive methods are difficult to implement compared to the resampling technique, as detailed knowledge of the algorithm is required if it is to be incorporated into an algorithm. Several cost-sensitive solutions have been discussed in the literature [22; 23; 24; 25; 26]. The algorithm-level solutions deal with imbalanced dataset by proposing a new algorithm or modifying an existing algorithm.