## 2 Related Work
### Imbalanced Data Classification  
Real-world datasets are mostly imbalanced and the lack of minority class samples can cause bias in classification models that have been constructed using classification methods [15]. Currently, imbalanced data classification methods can be divided into two groups: data-level and algorithm-level methods. There are many methods for solving imbalanced data classification problems at the data level, which can also mainly be divided into two types: undersampling [16; 17; 18] and oversampling [19; 20; 21]. However, although data-level methods can effectively deal with the imbalanced data problem, when data are extremely imbalanced, the excessive generation or reduction of data can alter the distribution of the original data while the limited generation or reduction of data cannot change the problem of data imbalance. Due to the high reliability of the LTE-R wireless communication system, datasets for the detection of LTE-R cells with degraded communication performance are extremely imbalanced. Therefore, algorithm-level methods should beconsidered for realizing the detection of cells with degraded performance. The main idea of algorithm-level methods is to directly modify the learning process of classification methods to improve sensitivity to minority classes. Recently, shallow-model algorithm-level methods have achieved great success in the fields of imbalanced data classification and fault detection [22; 23; 24]. However, they have significant limitations in processing high-dimensional data and expressing complex nonlinear patterns. The emergence of neural networks and deep learning technologies has provided new and effective ways to solve imbalanced data classification problems, and many new solutions for imbalanced data classification have emerged. [PERSON] et al. [25] applied the cost-sensitive loss function to a deep belief network (DBN) and utilized the evolutionary algorithm to find the optimal misclassification cost, thus realizing the classification of imbalanced data. [PERSON] et al. [26] proposed a cost-sensitive deep convolutional neural network (CNN) and experimentally demonstrated the superiority of this method in imbalanced time series classification. The above cost-sensitive method involves strengthening the recognition ability of the model to minority class samples by introducing the cost-sensitive factor in the training process. However, cost-sensitive factors are often difficult to determine, and these kinds of empirical risk minimization methods often have limited effectiveness when data are extremely imbalanced. Therefore, when the data are extremely imbalanced, some researchers utilize autoencoder (AE) neural networks or generative adversarial networks (GANs) to build data models for majority class samples. They then determine whether the data are abnormal based on the reconstruction error. [PERSON] et al. [27] discussed how to use AE and the variational autoencoder (VAE) neural network to detect abnormal data. [PERSON] et al. [28] utilized GAN to guide the training process of AE, which can help AE learn the data distribution of majority class data. [PERSON] et al. [29] proposed an improved autoencoder for unsupervised anomaly detection, which optimizes the anomaly detection task by integrating anomaly detection-based loss and autoencoder reconstruction loss. Furthermore, this method can learn representations that preserve local data structures to avoid feature distortion. But, these unsupervised anomaly detection methods will overlook the information contained in minority class samples. Given the above, constructing specific optimization objective functions to increase the distance between samples of different categories becomes an effective solution. [PERSON] et al. [30] designed a change detection algorithm based on Siamese networks for optical aerial images, and utilized an improved contrastive loss-focal contrastive loss (CFCL) to train the model, which can solve the data imbalanced problem. [PERSON] et al.