# An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
## 3 Results and Discussion
### The Comparison of the Improved Method with Other Methods  
To demonstrate the advantage of the proposed algorithm in this work, label consistent K-SVD (LC-KSVD) [47], sequential generalization of a k-means (SGK) [48], support vector machine (SVM), and the decision tree algorithm are well-known methods. Therefore, they were built as a comparison in this section. The study in [49] proposed a new classification model for the dictionary learning algorithm and applied it to the K-SVD and SGK. The parameters were set to 0.01. In this paper, these were called the KSVD-DL and SGK-DL. Typically, the dimension impacts the algorithm performance, which affects the training time and the size of the matrix. As a result, the performance of the algorithms with varied dimensions was analyzed in this study. Figure 13 shows the recognition accuracy of different algorithms and the improved algorithm. The best parameters of the improved algorithm changed with the increase in the dimension. Therefore, for different dimensions, the parameters were set to different values. The average accuracy of SVM was nearly 82.57%. The average accuracy of SVM was significantly worse than the improved algorithm, decision tree, and KSVD-DL algorithm. The average accuracy of the decision tree was nearly 87.99%. When the dimension of the sample was smaller than 10, the decision tree outperformed the improved algorithm and KSVD-DL algorithm. When the dimension of the sample was larger than 10, the average accuracy of the improved algorithm was greater than the other traditional algorithms and increased with the increase in dimension. Figure 14 and Table 3 show the comparison results between the different algorithms and the improved algorithm. The number of atomic columns of the improved algorithm was 18, and the average training time of the proposed algorithm was faster than the KSVD-DL algorithm by about 7.33 s and the SGK-DL algorithm by about 21.41 s.  
To prove the advantage of the improved algorithm, we performed the Nemenyi test to show the difference between the improved algorithm and other algorithms. \(p\) is the standard for significant difference [50]. When \(p<0.05\), both different algorithms are significantly different. Table 4 shows that our algorithm was significantly different from the SVM and decision tree. Although it is not significantly different from KSVD-DL, the proposed algorithm has certain advantages in other aspects such as the low average training time. When the dimension of the sample is larger than 10, the average recognition accuracy  
Figure 13: The recognition accuracy of the traditional algorithm and improved algorithm. of the improved algorithm is maintained above 90%, and the average training time of the algorithm is controlled within 4 s. The proposed algorithm achieved the highest average recognition accuracy among all algorithms included in the comparison. The experimental results showed that the algorithm proposed in this paper is a more effective method for an electronic nose than the traditional dictionary learning algorithms. For further comparison with other dictionary algorithms, the reconstruction residual error can be considered as the standard of the classification precision in this section. As shown in Figure 15a,b, it can be found that the 1 st reconstruction residual was smaller than the KSVD-DL by about 110. Since the proposed model imposes a constraint that the coefficients in the same category should keep a large coefficient and be closer to their class centers, it allows for the reconstruction of the same category to be more precise. The result demonstrates that the improved method had a better classification precision. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **SVM** & **Decision Tree** & **KSVD-DL** \\ \hline \(p\) & 0.001 & 0.036 & 0.372 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The experimental results of the Nemenyi test. Figure 14: The training time of the traditional algorithm and improved algorithm.