## 5 The Application in Fatigue Detection  
The above experiments verify that the SQGA optimized ensemble learning algorithm proposed in this paper can obtain better accuracy in dealing with the binary classification problem. In this section, the algorithm is applied to real fatigue detection. The Figure 7 gives the flow chart of fatigue detection. The whole process is mainly divided into four stages: MFCC extraction, training dataset acquisition, ensemble learning to establish the detection model and detection stage. MFCC extraction stage: collect the voice of controllers and extract the speech by MFCC. Training dataset acquisition: label the MFCC processed information according to the controller's own feelings and obtain the original training dataset. Ensemble learning stage: use the ensemble learning method proposed in Section 3 for establishing the detection model. Detection stage: Collect real-time voice data from the controller and extract it by MFCC. Input the processed data into the detection model, which then judges the controller's fatigue state. The voice data from land-air communication used in this paper comes from a front-line control unit in China, including the voice data from the unit's tower, approach and controllers of regional control positions. The date of voice data recordings is from 3-7 April 2022 and from 16-21 April 2022, from 2:00 to 24:00 every day. According to the structural characteristics of land-air communication, the voice data from the controller in the land-air communication data was intercepted. Then, the corresponding MFCC was obtained by speech preprocessing technology. The fatigue state of voices and the relevant information corresponding to the controllers are marked according to the controllers' own feelings. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Classification Accuracy (\%)**} \\ \cline{2-5}  & **Bagging** & **Boosting** & **Vote** & **SQGA** \\ \hline German & 79.5 & 79.5 & 80.4 & 82.4 \\ Ionosphere & 88.1 & 89.4 & 90.4 & 91.8 \\ Image & 98.4 & 98.2 & 98.8 & 99.2 \\ Thyroid & 96.4 & 96.9 & 97.5 & 98.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy results of ensemble algorithm. Figure 7: The flow chart of fatigue detection. The original dataset includes 158,620 samples. The ratio of normal samples and fatigue samples is about 10:1. In this experiment, the training samples were selected according to the ratio of 10:1. A total of 5000 normal samples and 500 fatigue samples were randomly selected from the training set. The test samples consisted of 1000 normal samples and 200 fatigue samples. The detection experiments were carried out based on the single machine learning method (SVM is selected here), ensembled learning algorithms, bagging and boosting and the method in this paper. The base classifiers ensembled by bagging and boosting algorithms are SVM. The false positive rate, false negative rate and detection accuracy of the whole body were compared. Table 4 shows the test results:  
From the detection results obtained in the above table, it can be seen that the false positive rate and false negative rate of fatigue detection with the SVM algorithm are higher than those of the ensembled algorithm. We can also see the advantages of ensemble learning. Among the three ensemble algorithms, the proposed algorithm can achieve the best detection effect, which is consistent with the experimental results obtained on the UCI dataset. By increasing the differences between base classifiers, the generalization performance of the algorithm is improved. The training time of SVM is 3.2 s, and the grid search to find the optimal parameters costs a lot of time. Ensemble learning methods are not sensitive to parameters and can greatly reduce computational complexity. For the testing time, as the single SVM obtains the simplest model, it only cost 0.13 s, while the proposed method cost 0.36 s. This is also good proof of the \"no free lunch\" theory.