# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 2 Related Work
### Research on Imbalanced Data Classification at the Algorithm Level
#### 2.2.2 Ensemble Learning Method  
The main idea of the ensemble learning method is to train multiple base learners and then to coordinate the results of multiple base learners to obtain the final result. Ensemble learning is used for imbalanced data classification, mainly because of its concept of integration. It combines the preferences of multiple classifiers and combines classifiers with existing imbalanced data classification methods to deal with the task of imbalanced data classification [26].  
Ensemble learning methods can be divided into heteromorphic and homomorphic types, according to the category relationship between basic classifiers [27]. Heteromorphic ensemble learning refers to the use of a variety of different classifiers for integration, whereas homomorphic ensemble learning refers to the integration of the basic classifier with the same kind of classifier with different parameters. Different integration methods have their own advantages in the classification of imbalanced data.  
In heteromorphic integration methods, each basic classifier has its own unique characteristics; therefore, for a particular type of data set, one of the basic classifiers will be more efficient than the others. The heteromorphic ensemble learning method is always combined with a cost-sensitive method to change the weight of each base classifier in the next iteration according to the performance of each base classifier in each iteration until the final result is integrated at the end of the iteration. The classical algorithm is AdaBoost [17; 28; 29]. AdaBoost has multiple iterations, with each iteration learning a classifier and updating the weight of the sample based on the performance of the current classifier. The update strategy is as follows. In each iteration classification, the weight of the correctly classified samples is reduced, and the weight of the incorrectly classified samples is increased. The final model is a weighted linear combination of multiple iteration models. AdaCost [30] modifies the weight-update strategy of the AdaBoost algorithm. In AdaBoost, the weight of the correctly classified sample is reduced to the same proportion, and the weight of all misclassified instances is also increased by the same proportion. By contrast, the adjustment strategy of AdaCost is to greatly increase the weight of the misclassified samples at a high cost. Researchers have also proposed many improved versions of AdaCost, such as AdaCI, AdaC2, and AdaC3 [31].  
In the homomorphic ensemble learning method, the strategy for imbalanced data is to combine sampling and integration, to generate multiple subsets through a series of sampling of the original training set, then to train the base classifier with different subsets, and finally to output the final result through voting or merging. Typical algorithms include SMOTEBoost [32]. The method is based on an imbalanced data processing method, and the sampling technology is embedded in the boosting algorithm, which changes the data distribution used to train the next classifier in the direction of small classes. Some studies have shown that the integration interval is the key factor influencing the integrated learning classification performance [33]. [PERSON] et al. [34] combined SMOTE with bagging, boosting, and other methods, and proposed a method designed to change the sample weight by changing the data sampling interval, which can improve the classification accuracy of a few types of samples.