## 2 Related Works
### Class Imbalance Problem  
Whether in academia or industry, imbalanced learning has attracted increasing attention. In the real world, this class imbalance problem exists in many application fields, such as anomaly detection, credit card fraud detection, and fault diagnosis, etc.  
Many researchers have made efforts to solve the class imbalance problem and have achieved various results. These studies are classified into two main categories. One category functions on data level. These methods create balanced datasets by reducing the majority class (undersampling) or increasing the minority class (oversampling). The most famous resampling method is SMOTE [28]. By analyzing the characteristics and distribution of the minority class, SMOTE generates new samples and adds them to the dataset. Although SMOTE increases the number of minority class samples and improves the classification performance, it takes extra time to generate new samples and the procedure can generate noise [11]. The undersampling method generates a balanced dataset by reducing the sampling rate of other class samples. However, the undersampling method causes information loss, and therefore some studies use a hybrid sampling method. [PERSON] et al. [29] proposed a novel method named fuzzy logic-based oversampling (FOS) to achieve a class imbalanced distribution through an information decomposition algorithm based on fuzzy logic [30] and, then, combined this method with random undersampling and random oversampling and utilized ensemble learning to conduct spam detection on Twitter [11].  
Another way to deal with the class imbalance problem is from the algorithm perspective. Algorithm-level solutions do not cause changes in data distribution, and therefore they are suitable for multiple types of imbalanced datasets [31]. The typical algorithm-level method is cost-sensitive learning. Cost-sensitive learning optimizes an algorithm by considering the cost differences in distinct misclassification situations and assigns costs to the corresponding types, allowing the algorithm to achieve better performances on class imbalanced data [31]. Cost-sensitive learning is popular for addressing unknown varying costs in class imbalance problems at the algorithm level. MetaCost [32] is a reweighting algorithm proposed by [PERSON] in which the basic idea is to use the Bayes risk theory to reweight instances in the training dataset based on the optimal cost classification. The AdaCost algorithm [33] is an improvement to the AdaBoost classification algorithm; it obtains the cost-sensitive classification by reweighting. WSNN [34] is a class imbalance method that uses cost as a weight distributed to the minority classes to improve the final classification accuracy. [PERSON] et al. [35] embedded the cost information into a modified cross entropy loss function during prediction to solve the imbalance and skewness challenge in hospital readmission prediction. [PERSON] et al. [36] proposed an evolutionary cost sensitive deep belief network (ECS-DBN) for imbalanced classification, which optimizes the misclassification cost based on the training data by using adaptive differential evolution. [PERSON] et al. [37] decomposed the F-measure optimization into a series of cost-sensitive classification problems, and investigated the cost-sensitive feature selection by generating and assigning different costs to each class.