# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 5 Discussion and Future Work  
Imbalanced data classification is concerned with the performance of learning algorithms in the presence of imbalanced data categories, and this problem has become a popular research topic in the field of machine learning. Traditional processing methods are based only on the data level or algorithm level for a single improvement. In this paper, we proposed a cost-sensitive VAE classifier method that combines the advantages of algorithm-level and data-level approaches. * To address the imbalance of data categories, this method incorporates a cost-sensitive factor so that the misclassification cost of minority-class samples is high and that of majority-class samples is low. Then, the overall misclassification cost is used as the model optimization target to improve the classification accuracy of the minority class. The results of Experiment 1 show that, in the task of amorphous alloy material classification, the cost-sensitive VAE outperforms other algorithms that favor a particular class of metrics in a comprehensive way. The F1-score, Precision, and Recall metrics are higher for each class of the data. Among them, the F1-score, Precision, and Recall for the minority classes (BMG) were 0.9432, 0.9644, and 0.9239, respectively. * Through Experiment 2, it was shown that the F1-score, Precision, and Recall of the three types of data classifications also increased for increasing values of the adjustment factor \(\alpha\), indicating that the cost-sensitive factor has an effect on the classification of the imbalanced data. For the minority class, the three evaluation criteria grew faster. The traditional method of designing the cost matrix is closely related to the number of samples, and generally takes the number of samples or the proportion of the number of samples as its cost, which cannot accurately reflect the real category distribution characteristics of the data. The proposed method combines the domain knowledge to design the cost matrix, and refines the cost-sensitive factor to make the cost matrix design more reasonable. The experimental results show that the method introducing correct domain knowledge results in a better F1-score, Precision, and Recall than other methods introducing wrong domain knowledge in the classification task of amorphous alloy materials. The results of Experiment 3 show that the algorithm introducing the cost matrix of correct domain knowledge exhibited a better F1-score, Precision, and Recall than the other five algorithms that introduced incorrect domain knowledge. The experimental results show that the correct cost matrix with domain knowledge can achieve a better classification effect, and that the embedding cost matrix with domain knowledge is effective. Despite its performance, the cost-sensitive VAE has some limitations. The algorithm used in this study is a cost-sensitive variational classifier algorithm that incorporates the three cost sensitive parameters H, M, and L. In this study, we learned the size relationship of the three parameters through domain knowledge, so we introduced the adjustment factor \(\alpha\) to set the corresponding equality relationship for the three parameters, and then determined the size of the three parameters through the grid search method. This method can be very time-consuming and labor-intensive when faced with the problem of having a very large search space. Consequently, the following two points may be considered in future research. * Future studies can consider methods to learn the size of the three parameters H, M, and L through the algorithm adaptively; that is, methods designed to adaptively acquire domain knowledge adaptively. * In addition to the function of the cost-sensitive VAE classifier used in this study, the cost-sensitive VAE classifier can also generate new data by sampling the hidden variable \(Z\) and placing it into the decoder. Therefore, subsequent studies can also investigate how to compensate for the shortcomings of imbalanced datasets by generating new data.