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Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Authors to whom correspondence should be addressed.
Sensors 2018, 18(10), 3209;
Received: 13 August 2018 / Revised: 7 September 2018 / Accepted: 12 September 2018 / Published: 23 September 2018
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
PDF [1302 KB, uploaded 23 September 2018]


This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods. View Full-Text
Keywords: drift compensation; transfer learning; domain correction; electronic nose drift compensation; transfer learning; domain correction; electronic nose

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Tao, Y.; Xu, J.; Liang, Z.; Xiong, L.; Yang, H. Domain Correction Based on Kernel Transformation for Drift Compensation in the E-Nose System. Sensors 2018, 18, 3209.

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