# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 1 Introduction
### Context of the Study  
In general terms, the minority class often illustrates samples of higher importance and interest rather than the majority class. Nevertheless, compared to the minority class, themajority class usually has a more significant number of samples in a meaningful way, and sometimes, the situation may be extremely serious.  
Different situations can occur in confronting the imbalanced datasets, and four common cases are depicted in Figure 1, where the blue-filled circles represent the samples of the majority class; in contrast, the red circles denote the minority class [1]. It has been shown that the type of data complexity is the principal determining factor of classification performance reduction [2].  
Most of the classical classification methods, such as decision trees [2; 3; 4], KNN [5; 6], and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [7; 8], usually train models that maximize the accuracy of proposed algorithms, sometimes ignoring the minority class [9; 10; 11]. Hence, several techniques have been designed and implemented to handle the imbalanced binary classification problems. Among these techniques, oversampling and undersampling are well-known [12; 13; 14; 15]. Yet, the common undersampling and oversampling algorithms modify the initial class distribution of the dataset by excluding the majority class samples or expanding the minority class samples.  
Cost-sensitive learning algorithms were among the solutions for the above-mentioned issues of imbalanced data [16; 17; 18]. Such algorithms designate misclassification cost errors for multiple classes, mainly lower costs for the samples of the majority class and higher for the minority class. In addition, Bagging [19] and Boosting [20] methods, which are based on ensemble learning algorithms, are among the other commonly used methods to handle imbalanced class problems [7; 8; 21; 22].  
In this paper, we use several undersampling and oversampling methods in the process of implementing our methodology, which is briefly introduced in the sequel:  
1. RUS: Among undersampling methods, random undersampling (RUS) is the simplest one, in which the samples of the majority class are randomly removed until suitable balanced data are obtained [23].
2. Tomek Links: Some of the undersampling techniques focus on overlap elimination. For example, the Tomek Links [24] method, which is a modification of the Condensed Nearest Neighbor rule, is one of these methods.
3. One-Sided Selection: As a development of the Tomek Links algorithm, one can refer to the One-Sided Selection or briefly OSS method [25] that merges Tomek Links and the Condensed Nearest Neighbor algorithms.
4. Near Miss is another popular undersampling method that randomly removes the majority of class samples. When two samples classified in different classes are very close to each other, it removes the sample belonging to the larger class [5].
5. ROS: Among the oversampling algorithms, random oversampling (ROS) is the simplest one that merely selects and copies the samples from the minority class randomly, leading to more balanced data [26].
6. SMOTE: The best-known oversampling method is the Synthetic Minority Oversampling Technique (SMOTE) [14; 27; 28] which leverages the kNN algorithm to identify the neighbors of minority class samples and generates the new sample by selecting the kth neighbor randomly [29].  
Figure 1: The schematic diagram of several data distributions in various scenarios with two-dimensional binary-class-imbalanced data. (**a**) Imbalanced Ratio, (**b**) Class Surrounding, (**c**) Class Overlapping, (**d**) Small Disjucts.