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Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System
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 dri...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721181/ https://www.ncbi.nlm.nih.gov/pubmed/31430909 http://dx.doi.org/10.3390/s19163601 |
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author | Liu, Tao Li, Dongqi Chen, Jianjun Chen, Yanbing Yang, Tao Cao, Jianhua |
author_facet | Liu, Tao Li, Dongqi Chen, Jianjun Chen, Yanbing Yang, Tao Cao, Jianhua |
author_sort | Liu, Tao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6721181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67211812019-09-10 Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System Liu, Tao Li, Dongqi Chen, Jianjun Chen, Yanbing Yang, Tao Cao, Jianhua Sensors (Basel) Article 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. MDPI 2019-08-19 /pmc/articles/PMC6721181/ /pubmed/31430909 http://dx.doi.org/10.3390/s19163601 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Tao Li, Dongqi Chen, Jianjun Chen, Yanbing Yang, Tao Cao, Jianhua Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System |
title | Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System |
title_full | Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System |
title_fullStr | Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System |
title_full_unstemmed | Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System |
title_short | Active Learning on Dynamic Clustering for Drift Compensation in an Electronic Nose System |
title_sort | active learning on dynamic clustering for drift compensation in an electronic nose system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6721181/ https://www.ncbi.nlm.nih.gov/pubmed/31430909 http://dx.doi.org/10.3390/s19163601 |
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