# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 2 Related Work
### Research on Imbalanced Data Classification at the Algorithm Level
#### 2.2.1 Cost-Sensitive Method  
When dealing with the problem of imbalanced data classification, the traditional classifier algorithms treat the misclassification costs of different categories equally and aim to reduce the global classification error rate. Therefore, owing to the small number of samples of minority classes, the traditional classification algorithms have a low recognition rate of minority classes. However, in practical tasks, different classification errors result in different degrees of loss, and the cost of minority categories being misclassified is far greater than the cost of the majority categories being misclassified in general. For example, in medical diagnosis, the cost of misclassifying sick samples (minority samples) as healthy samples (majority samples) is much greater than that of misclassifying healthy samples (majority samples) as sick samples (minority samples). The basic idea of cost-sensitive learning is to assign a high misclassification cost to the minority classes and a low misclassification cost to the majority classes, and then to minimize the total misclassification cost as the training and optimization goal of the algorithm, so as to improve the classification performance of minority classes. At present, the research on cost-sensitive learning has mainly focused on two aspects: directed cost-sensitive learning and meta cost-sensitive learning. The main method of directed cost-sensitive learning is to change the internal structure of the traditional classifier model by introducing a cost-sensitive factor based on the traditional classification algorithm and transforming the traditional training goal of reducing the global classification error rate into the training goal of reducing the global classification error cost. Researchers have proposed cost-sensitive versions of various learning models, such as a support vector machine (SVM), decision tree, and neural network [16; 17]. For example, in [18], a new cost-sensitive decision tree approach that minimized the sum of misclassification costs while selecting the splitting attribute at each non-terminal node was developed. The performance of the proposed method was compared with that of the traditional classification model on a real credit card dataset, and better experimental results were obtained. Reference [19] assigned different costs to false positive errors and false negative errors as specified by a ratio to estimate a classifier that minimized the weighted error. In [20], the concept of a weighted SVM (W-SVM) was proposed to move the classification boundary to the majority classes to solve the defect of the SVM algorithm not being able to treat each class differently according to the importance of each class, and to improve the prediction accuracy of models for the data of minority classes. In [21], a cost-sensitive back-propagation neural network (CSBPNN) algorithm for multi-category imbalanced data classification was proposed. This algorithm introduced a misclassification cost such that the output threshold moved toward the higher cost classes to ensure that they were classified correctly. References [22; 23] proposed a novel cost-sensitive algorithm that introduced cost information not only in the prediction stage, but also in the deep learning model stage. Directed cost-sensitive learning makes the traditional classification algorithm suitable for imbalanced data by modifying the internal structure of the classifier. However, as some algorithms are difficult to modify directly, the concept of meta cost-sensitive learning has been proposed. On the premise of not changing the existing learning algorithm, meta cost-sensitive learning converts the misclassification cost of the class into the sample weight. It weights each sample according to the misclassification cost of the class to which it belongs, and then reconstructs the original dataset according to the weight. The disadvantage is that the process of reconstruction changes the distribution of samples and sometimes loses some useful sample information.