## 5 Result
### Comparative Experiment of Feature Extraction  
Due to the lack of data in some samples in the original data, the original 480,000-dimensional data was not used for comparison in this experiment. The experiment is mainly carried out by comparing the cropped data and the data after feature extraction. Table 6 shows the comparison of the computing time required before and after feature extraction, and Table 7 shows the difference in recognition accuracy before and after feature extraction:  
In this experiment, three traditional machine learning methods are selected to test the effect of feature extraction, including the semi-supervised learning algorithm SELMWK proposed in this paper. Three traditional supervised learning methods are linear support  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Set Size** & **SVM-Linear** & **SVM-rbf** & **LDA** & **SELMWK** \\ \hline \hline \multicolumn{5}{c}{Before feature extraction} \\
10 & \(5.5\times 10^{-2}\) & \(6.5\times 10^{-2}\) & \(2.6\times 10^{-1}\) & \(1.8\times 10^{-1}\) \\
50 & \(5.7\times 10^{-1}\) & \(1.0\) & \(1.5\) & \(1.4\) \\
500 & 1.2 & 1.4 & \(4.1\) & \(99.43\) \\ \hline \hline \multicolumn{5}{c}{After feature extraction} \\
10 & \(5.1\times 10^{-4}\) & \(4.6\times 10^{-4}\) & \(4.6\times 10^{-3}\) & \(1.9\times 10^{-3}\) \\
50 & \(6.9\times 10^{-4}\) & \(8.4\times 10^{-4}\) & \(6.3\times 10^{-3}\) & \(6.3\times 10^{-3}\) \\
500 & \(7.8\times 10^{-4}\) & \(1.2\times 10^{-3}\) & \(3.6\times 10^{-2}\) & \(1.1\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of computing time before and after feature extraction (s).  
Figure 3: TGS2612 5.65V after exponential moving average transformation under 250 ppm methane gas.  
vector machine (SVM-linear), support vector machine based on RBF kernel function (SVM-rbf) and LDA. The SELMWK algorithm all uses four labeled data for experiments.  
Table 6 mainly analyzes the aspect of the algorithm running time. Due to the reasons of the computer itself, it cannot be guaranteed that the environment of each running is the same, so the running time will vary, but the change is not large. The data collected in this experiment is an average of 20 runs. By comparing the running time before and after feature extraction, it can be seen that the algorithm's running time is significantly reduced. Especially for the SELMWK algorithm, the running time before and after feature extraction is 99.43 s and 0.11 s, respectively, which greatly shortens the model training time.  
Table 7 analyzes the recognition accuracy of the algorithm. The purpose of feature extraction is to reduce the computational complexity of the model and to grasp the key information points in the data. The feature extraction process cannot lose too much useful information in the original data. From the results, the feature extraction method used in this paper not only does not reduce the recognition accuracy of the algorithm but also increases the recognition accuracy of the algorithm, especially when the amount of data is small.  
From the above experiments, the feature extraction method used in this study is simple, effective and easy to implement. While extracting the key information of the data, the dimension of the data is greatly reduced, thereby reducing the cost of model training.