## 4 Results and Analysis
### Hyperparameter Selection of Classifiers  
Different classifiers have different hyperparameters that need to be preset. Hyperparameters play a vital role in the performance of classifiers. The main hyperparameters we consider are as follows:  
* For the SVM classifier, the main hyperparameters are the kernel function type (we selected the radial basis function, i.e., RBF), penalty coefficient \(C\), and RBF kernel function coefficient \(g\). The penalty coefficient \(C\) reflects the tolerance of the SVM for errors. The larger the value of \(C\), the higher the training accuracy, but the generalization  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline Actual positives & TPs (true positives) & FNs (false negatives) \\ Actual negatives & FPs (false positives) & TNs (true negatives) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Two-class confusion matrix.  
ability of SVM is weakened. The smaller the value of \(C\), the higher the tolerance of the SVM for errors, and the stronger the generalization ability of the SVM. The kernel function coefficient \(g\) determines the mapped feature space. The larger the value of \(g\), the higher the dimensionality of the kernel function mapping; the smaller the value of \(g\), the lower the mapping dimension.
* For the KNN classifier, the main hyperparameter is the number of neighbors (\(k\)) and the weight of neighbors at different distances. The larger the value of \(k\), the higher the accuracy of KNN, but oversized \(k\) will lead to overfitting. The smaller the value of \(k\), the lower the accuracy of KNN, but too small a value will lead to underfitting. The weight selection for neighbors with different distances is distance, which means that the weight of neighbors is inversely proportional to the size of the distance.
* For the DT classifier, the main hyperparameters are min sample split, min sample leaf, and criterion. Min sample split represents the minimum number of samples required to split internal nodes, min sample leaf represents the minimum number of samples required for an effective terminal node, and criterion represents the selection criteria of features.
* For the LR classifier, the main hyperparameters are the penalty parameter \(C\) and the optimization type solver. The penalty parameter \(C\) reflects the reciprocal of the regularization strength. The smaller the value of \(C\), the stronger the regularization. The solver represents the optimization type of the algorithm.
* For the GBDT classifier, the main hyperparameters are learning rate, n estimators, max depth, max features, min sample split, and min sample leaf. Among them, the learning rate is the weight reduction coefficient for each tree, n estimators is the number of trees, max depth is the maximum depth of the tree, max features is the number of features selected by the tree, and min sample split and min sample leaf are similar to the definition in DT.
* For the RF classifier, the main hyperparameters are n estimators, max depth, max features, min sample split, and min sample leaf. These hyperparameters are similar to the definitions in GBDT.
* For the XGBoost classifier, the main hyperparameters are learning rate, n estimators, max depth, max features, min sample split, and min sample leaf. These hyperparameters are similar to the definition in GBDT.  
We optimized the hyperparameters of each classifier via the grid search method [33]. The hyperparameters of the stacking ensemble model are optimized based on the optimized value of each individual classifier.