# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 3 Materials and Methods
### Sampling Algorithms for Imbalanced Data  
In a large number of papers, novel methods have been proposed to address the problem of imbalance between classes, which are classified into three groups [27]; algorithm-level approaches (in which a new algorithm is created or an existing one is modified), data-level approaches (in which data is modified in order to lessen the performance impact of the algorithms of classification when there is an imbalance in the distribution of classes), and cost-sensitive classification (which consider different costs with respect to the class distribution).  
This section deals with the class balancing algorithms that will be evaluated in the present investigation. All of them belong to the data-level approach for imbalanced classification. First, we address oversampling algorithms and then, undersampling algorithms. At last, we address hybrid approaches.  
It is possible to find several state-of-the-art articles [48; 49; 50; 51; 52] in which the preprocessing of datasets is employed to reduce the impact caused by the distribution of classes. In such research, it has been empirically demonstrated that the application of a preprocessing stage to balance the distribution of classes is usually a useful solution to improve the quality of the identification of new instances. Data preprocessing techniques for imbalanced data can be divided into three groups:  
1. Oversampling algorithms. These techniques are based on the creation of synthetic instances of the minority class through replication, or creating new instances based on the existing ones.
2. Undersampling algorithms. These methods are based on the elimination of instances of the majority class.
3. Hybrid Algorithms These methods are a combination of oversampling and undersampling techniques.  
Oversampling algorithms seek to match the quantities of instances in each class by oversampling minority classes. In this way, the quantity of instances in these classes will be artificially increased, making all classes have approximately the same number of objects. The techniques for selecting instances for oversampling that will be used for the comparative analysis carried out in this work are listed below (Table 3).  
As mentioned earlier, undersampling algorithms seek to match the amounts of instances in each class, by sampling the majority classes [25]. Thus, objects that are considered less relevant are eliminated, so that all classes have approximately the same number of instances. Next, the undersampling algorithms evaluated in the present investigation are listed (Table 4).  
Hybrid algorithms use oversampling and undersampling techniques. The hybrid algorithms evaluated in the present investigation are (Table 5):In this section, we analyzed the datasets to be used, as well as some of the most representative associative classifiers. In addition, we mentioned the sampling algorithms for class balancing we will use. Next section explains the proposed experimental methodology in order to assess the impact of the preprocessing techniques for imbalanced financial data sampling in the performance of classifiers of the associative approach.