## 5 Experiments and Discussion
### Comparison and Discussion  
In this section, comparative experiments on the detection dataset of LTE-R cells with degraded communication performance are presented in order to verify the performance of the method proposed in this paper. The experimental results are discussed and analyzed. Before conducting the comparative experiments, we first verified the effectiveness of the proposed two-stage training method on the experimental dataset. Figure 9 illustrates the classification performance of the model after training it 10 times on the training and test sets, both with and without using the two-stage training method. As can be seen from Figure 9, the two-stage training method proposed in this paper can significantly improve the training performance of the model compared to the training method that only uses contrastive loss and SGD (one-stage training). This is because the  
Figure 8: The grid search results of \(G\) and \(NP\). (**a**) shows the grid search results for \(G\) and \(NP\) in the form of a heat map, and the darker the color, the higher the F1 score. (**b**) shows the grid search results for \(G\) and \(NP\) in the form of a group bar chart, and the longer the length of the bar, the higher the F1 score. Figure 9: Comparison of different training methods. use of the differential evolution algorithm compensates for the deficiency of contrastive loss, which only focuses on between-class scatter. When conducting the comparative experiments, several imbalanced data classification methods, such as SMOTE-SVM [41], ADASYM-SVM [41], Cosen-SVM [42], and several anomaly detection methods, such as LOF [7], VAE [14], and DEDAE [3] are selected as comparison methods. Considering that the detection of LTE-R cells with degraded communication performance is a sequence data classification problem, we also selected two sequence data classification approaches for performance comparison: the fast shapelet tree (FST) proposed by [PERSON] et al. [43] and the learning time series (LTS) proposed by [PERSON] et al. [44]. Furthermore, considering the imbalance of the experimental data, a CNN-based imbalanced sequences classification approach, called the cost-sensitive convolution neural network (CSCNN) [26], was also selected as a comparison approach. To verify the effectiveness of the proposed approach for feature extraction, we designed experiments to compare the feature extraction capabilities. Figure 10 shows the distribution of features extracted by our approach, FST, LTS, CSCNN, VAE, and DEDAE on the degradation detection dataset in the feature space. Figure 10 illustrates the feature data after PCA dimensionality reduction. It can be seen that the features extracted by the proposed feature extraction neural network were able to distinguish between normal and abnormal cells at the feature level. This is because the proposed optimization objective, based on the Fisher criterion, can minimize the within-class scatter of extracted features while maximizing the between-class scatter. The features extracted by the other five comparison approaches could not clearly distinguish between the two types of samples at the feature level, which affected the accuracy of classification. The experiments showed that the proposed approach has obvious advantages over the comparison approaches when extracting imbalanced sequence features. Then, in order to verify the classification performance of the proposed approach, we compared the classification performance of our model to baselines on the degradation detection dataset. The evaluation metrics for classification performance include G-mean, AUC, F1 score, and precision. The calculation process of each evaluation metric can be found in [45]. During the experiments, each approach trained the model on the training set and used the trained model to calculate the various evaluation metrics on the test set. After repeating the above process five times, we could obtain the mean and standard deviation of the different evaluation metrics for each approach.