# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details
### Ensemble Learning Model
#### SVM model  
We adopt 10-fold cross validation. After model parameters were optimized by grid search method, the average classification accuracy score was 0.8852. The distance between each sample and the classification hyperplane s shown in Table 8. The _SupportVectorClassifier()_ in sklearn was called to build this model. And _decision_function()_ was used to calculate. According to the four models' training results, the classification accuracy of the four basic classifiers are different. The SVM and BP neural network classification performance are better than K-NN and Bayes. However, the greater the difference in the base classifiers, the better the performance of the ensemble model in heterogeneous ensemble process. The ensemble database of the air traffic situation in East China was built by Tables 5-8. In Table 9, the four classification results are combined into a new ensemble learning dataset. It was used to train the air traffic situation assessment model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Positive** & **Negative** \\ \hline
1 & 1.00000000 & 7.46429951 \(\times\) 10\({}^{-13}\) \\
2 & 1.00000000 & 7.07690019 \(\times\) 10\({}^{-21}\) \\
3 & 1.0000000 & 5.88921171 \(\times\) 10\({}^{-20}\) \\
4 & 4.31728731 \(\times\) 10\({}^{-2}\) & 9.56827127 \(\times\) 10\({}^{-1}\) \\
5 & 9.99924099 \(\times\) 10\({}^{-1}\) & 7.59010962 \(\times\) 10\({}^{-5}\) \\ \(\cdots\) & & \(\cdots\) \\
189 & 4.90876080 \(\times\) 10\({}^{-1}\) & 9.12391981 \(\times\) 10\({}^{-1}\) \\
190 & 9.76546933 \(\times\) 10\({}^{-1}\) & 2.34530674 \(\times\) 10\({}^{-2}\) \\
191 & 9.80120565 \(\times\) 10\({}^{-1}\) & 1.98794345 \(\times\) 10\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The positive and negative prior probability. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Value** \\ \hline
1 & 0.99478238 \\
2 & 0.99806342 \\
3 & 0.99982834 \\
4 & 0.74293931 \\
5 & 0.97827422 \\ \(\cdots\) & \(\cdots\) \\
189 & 0.98293848 \\
190 & 0.32949282 \\
191 & 0.99993762 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The activation function. In this paper, we chose the SVC as the basic framework of the ensemble learning model. The grid search was used to search for optimal parameters. The 10-fold cross validation and mean square error were set as parameters. The main parameters of SVC were finally determined as follows: _kernel = 'rbf'_, _gamma = 0.0083_, _cache_size = 6000_, _C = 1.0_. And we used the confusion matrix to compare the classification effect of the ensemble learning model and single classification model. In Table 10, the ensemble learning model performed optimally in all indexes. Its average classification accuracy is up to 0.98. Recall and precision both exceed 0.95. The ensemble model's classification ability has obvious advantages. It indicates that the ensemble learning method can effectively improve the assessment accuracy of the air traffic network situation assessment model. According to the relationship between recall and precision, the PR graph was drawn in Figure 5. The PR curve of the ensemble model is more outside than others. And the optimal equilibrium point is the highest. It indicates that the ensemble learning model is superior to the others in balancing precision and recall. There is a crossover between SVM and the BP neural network's PR curve. And the position gap of the PR equilibrium point is small. It indicates that the classification performance of SVM is close to the BP neural network. The Bayes model performed worse in precision and recall than the other classifiers. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** \\ \hline K-NN & 0.8257 & 0.8094 & 0.7947 \\ Bayes & 0.7939 & 0.7631 & 0.7493 \\ BP neural network & 0.8702 & 0.8552 & 0.8419 \\ SVM & 0.8853 & 0.8947 & 0.8750 \\ Ensemble & 0.9898 & 0.9566 & 0.9743 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The recall and accuracy.