# A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection
## 3 Results and Discussion
### Secondary Feature Optimization  
In this experiment, we combine the compensated sensor values with the original sensor data remaining from the first feature optimization to construct a new dataset, which is subjected to the second feature optimization, and the results obtained are shown in Figure 6. Observing the sensor score graph of the second feature optimization, we can see that after the second feature optimization, the sensors with high scores before compensation are 0 in the new round of feature optimization scores. The condition of 45\({}^{\circ}\)C has the most outstanding results, and the sensors of W1S, W1W, W2W, and W3S before compensation have scores of 0, and only W5S has a high score. And the sensors showed a better aggregation trend, the high scoring sensors tend to be only about three, the other sensors are scored low, and even most of the scores are 0. The secondary feature optimization is conducive to further reduce the complexity of the mathematical model. And the compensated sensor scores also show a more concentrated trend, which is in line with our earlier mentioned property that the concentration of sensor scores is positively correlated with the electronic nose classification effect and the reduction in the influence of the drift factor. At the same time, it also shows that the influence of the drift factor on the feature optimization exists, and our proposed quadratic feature optimization can effectively reduce the influence of the drift on this part. \begin{table}
\begin{tabular}{c c c c} \hline
**Experimental Condition** & **Training Set** & **Validation Set** & **Statuses** \\ \hline
35\% RH & 92\% & 76\% & Before compensation \\
50\% RH & 96\% & 76\% & Before compensation \\
65\% RH & 89.3\% & 88\% & Before compensation \\
45 \({}^{\circ}\)C & 97.3\% & 80\% & Before compensation \\
35\% RH & 97.3\% & 84\% & After compensation \\
50\% RH & 88\% & 92\% & After compensation \\
65\% RH & 98.7\% & 96\% & After compensation \\
45 \({}^{\circ}\)C & 94.7\% & 88\% & After compensation \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison table of compensation results. In this experiment, in order to distinguish the results of the secondary feature optimization and to directly compare the effect of the processing method on the anti-drift performance of the electronic nose through the data, three different groups will be used to compare the classification correctness with the pre-compensation:  
The first group: once feature optimization compensation group. The second group: the unweighted secondary feature optimization group, the change group will compensate the data with the first feature optimization uncompensated original data combination of new data set, the new data set of secondary feature optimization. The third group: weighted secondary feature optimization group, on the basis of the second group, the compensated data will be weighted (preferentially selecting the compensated sensor data set for feature optimization). The obtained comparison Table 5 is shown below. Comparing the data in the table, the results of the secondary feature optimization can effectively further improve the classification efficiency of the electronic nose, in which the results of the training set are better than those of the primary feature optimization, while the validation set is improved by 8% in the 45 \({}^{\circ}\)C group, which makes the classification correctness reach 96%, presenting a good compensation effect. The effectiveness of the secondary feature optimization is proved, which further improves the robustness and anti-drift ability of the electronic nose. In the comparison of the groups with and without weighted secondary feature optimization, the results are not much different, and the results of the unweighted group in the training set are slightly better than those of the weighted group, but the number of sensor arrays is much less than that of the weighted group.