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Open AccessArticle

Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System

School of Microelectronics and Communication Engineering, Chongqing University, No. 174 Shazheng Street, Chongqing 400044, China
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Sensors 2019, 19(16), 3601; https://doi.org/10.3390/s19163601
Received: 12 July 2019 / Revised: 11 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
(This article belongs to the Special Issue Electronic Noses)
Drift correction is an important concern in Electronic noses (E-nose) for maintaining stable performance during continuous work. A large number of reports have been presented for dealing with E-nose drift through machine-learning approaches in the laboratory. In this study, we aim to counter the drift effect in more challenging situations in which the category information (labels) of the drifted samples is difficult or expensive to obtain. Thus, only a few of the drifted samples can be used for label querying. To solve this problem, we propose an innovative methodology based on Active Learning (AL) that selectively provides sample labels for drift correction. Moreover, we utilize a dynamic clustering process to balance the sample category for label querying. In the experimental section, we set up two E-nose drift scenarios—a long-term and a short-term scenario—to evaluate the performance of the proposed methodology. The results indicate that the proposed methodology is superior to the other state-of-art methods presented. Furthermore, the increasing tendencies of parameter sensitivity and accuracy are analyzed. In addition, the Label Efficiency Index (LEI) is adopted to measure the efficiency and labelling cost of the AL methods. The LEI values indicate that our proposed methodology exhibited better performance than the other presented AL methods in the online drift correction of E-noses. View Full-Text
Keywords: active learning; drift counteraction; dynamic clustering; electronic nose active learning; drift counteraction; dynamic clustering; electronic nose
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Liu, T.; Li, D.; Chen, J.; Chen, Y.; Yang, T.; Cao, J. Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System. Sensors 2019, 19, 3601.

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