Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Tao, Li, Dongqi, Chen, Jianjun, Chen, Yanbing, Yang, Tao, Cao, Jianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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
_version_ 1783448288140197888
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
work_keys_str_mv AT liutao activelearningondynamicclusteringfordriftcompensationinanelectronicnosesystem
AT lidongqi activelearningondynamicclusteringfordriftcompensationinanelectronicnosesystem
AT chenjianjun activelearningondynamicclusteringfordriftcompensationinanelectronicnosesystem
AT chenyanbing activelearningondynamicclusteringfordriftcompensationinanelectronicnosesystem
AT yangtao activelearningondynamicclusteringfordriftcompensationinanelectronicnosesystem
AT caojianhua activelearningondynamicclusteringfordriftcompensationinanelectronicnosesystem