# An Improved Algorithm of Drift Compensation for Olfactory Sensors
## 6 Conclusions  
Inspired by the DAELM algorithm, this study combines the domain transformation algorithm with the semi-supervised learning algorithm and proposes the DTSWKELM algorithm to compensate for sensor drift. First, by using MMD to represent the distance between two distributions, by minimizing MMD, a new domain is found, and the source domain data and the target domain data are mapped, thereby reducing the source domain data and the target domain data. The distribution difference between the data, the obtained new domain data is sent to the SWKELM model, the semi-supervised classifier is trained, and finally the target domain data is identified.  
Figure 6: The impact of the trade-off parameters \(\lambda_{1}\) and \(\lambda_{2}\) on the algorithm recognition effect. (**a**) The influence of \(\lambda_{1}\) on the algorithm; (**b**) the influence of \(\lambda_{2}\) on the algorithm.  
In the analysis stage of the experimental results, three groups of comparative experiments are mainly set up. First, the PCA method is used to compare and analyze the distribution of different batches of data before and after domain conversion, which more intuitively shows the impact of the sensor drift problem and verifies the effectiveness of the domain conversion process. Next, when testing the performance of the DTSWKELM algorithm, two experiments were set up. The first experiment is to set Batch1 as the source domain data, the data of Batch2-Batch10 are set as the target domain data and predict it. The second experiment is to set BatchN\(-\)1 as the source domain data and BatchN as the target domain data and make predictions on it. In these two groups of experiments, seven commonly used sensor drift compensation algorithms and SWKELM algorithm are used as control algorithms. Compared with other algorithms, the DTSWKELM algorithm proposed in this study has better recognition effect and can better deal with the long-term sensor drift problem. The last part is an analysis of the hyperparameter settings in the model. By setting different hyperparameters for comparative experiments, it shows the importance of hyperparameters to the model.  
Conceptualization, W.Z. and S.L. ([PERSON]); methodology, B.Y. and L.Y.; software, J.G.; validation, S.L. ([PERSON]); formal analysis, J.G. and L.Y.; investigation, B.Y.; resources, J.G. and S.L. ([PERSON]); data curation, J.G.; writing--original draft preparation, S.L. ([PERSON]), M.L. and L.Y.; writing--review and editing, S.L. ([PERSON]), M.L., S.L. ([PERSON]) and L.Y.; visualization, S.L. ([PERSON]) and J.G.; supervision, B.Y.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
Support by the Sichuan Science and Technology Program, 2021 YFQ0003.  
Not applicable.  
The original contributions presented in the study are publicly available. These data can be found here: [[https://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations](https://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations)]([https://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations](https://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations)) (accessed on 4 March 2022).  
The authors declare no conflict of interest.