## 5 Simulation and Performance Analysis
### Classifier Performance Analysis  
The dataset contains a total of 31,260 samples with bit error packet loss, out of which 23,267 samples were used for testing purposes. Additionally, there are 18,680 samples indicating congestion-related packet loss, among which 6000 were utilized for testing. The ensemble learning model was trained iteratively for a total of 1820 times. During the experiment, multiple independent repeated trials were conducted on the dataset and cross-validation was performed using test sets to obtain average detection results for each dataset in order to enhance the impartiality of experimental outcomes. To optimize the performance of the heterogeneous ensemble model, an analysis was carried out on individual learners' predictive abilities, resulting in accuracy measures such as recall rate precision and false alarm rate being obtained for each learner (Table 7). Table 7 reveals that the NB exhibits a low accuracy and precision rate, along with a high false alarm rate of 0.557. This indicates that when the algorithm is employed for distinguishing packet loss, a significant number of congestion loss packets are misclassified as bit error loss packets, posing substantial congestion risks to the network. Consequently, the naive Bayes method was preliminarily excluded from consideration. Moreover, in order to achieve more accurate prediction results, it becomes imperative to select individual classifiers with greater diversity. In this study, we utilized Q statistic value and DF value to quantify the dissimilarity among individual classifiers (as presented in Tables 8 and 9). Furthermore, it can be observed from Table 8 that the Q statistic value of the BP neural network is 0.9, ranking second-highest. Table 9 indicates that the DF value of the BP neural network is 0.128, which is the highest among all classifiers considered. Additionally, considering algorithm complexity and false alarm rate, the BP neural network does not \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Individual** & **Hyperparameters** & **ACC** & **REC** & **PRE** & **FAR** \\ \hline \multirow{4}{*}{LSSVM} & Punishment coefficients are 10, & & & & \\  & \(\gamma=0.4\); the kernel function is the & 0.821 & 0.795 & 0.816 & 0.245 \\  & radial basis kernel function & & & & \\ KNN & The number of neighbors is 7 & 0.887 & 0.874 & 0.882 & 0.184 \\  & Type of regularization is L2, the & & & & \\ LR & inverse of the regularization & & & & \\  & strength is 1000, the type of the & 0.775 & 0.730 & 0.778 & 0.392 \\  & solver is Llibinear & & & & \\  & The split node criteria are & & & & \\  & information entropy, the maximum & & & & \\  & depth of the tree is 11, the & & & & \\ DT & minimum number of samples & & & & \\  & needed at leaf nodes is 3, the & 0.869 & 0.855 & 0.864 & 0.173 \\  & minimum number of samples to & & & & \\  & split an internal node is 29, and the & & & & \\  & split strategy at the node is best & & & & \\  & 1 input layer, 1 hidden layer, 30 & & & & \\  & nodes, Relu activation function, 1 & & & & \\ BP neural & output layer, using Sigmoid & 0.823 & 0.832 & 0.803 & 0.327 \\ network & activation function, using & & & & \\  & binary\_crossentropy as the loss & & & & \\  & funQction and Adam Optimizer & & & & \\ [PERSON] & None & 0.743 & 0.841 & 0.732 & 0.557 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hyperparameters of individual learners and their performance in the dataset. perform optimally in discriminating data packet loss; hence, it was excluded from the final selection scheme for individual classifiers, leaving four types: LSSVM, DT, LR, and kNN.