Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines

Liu, T; Zhang, W; McLean, P; Ueland, M; Forbes, SL; Su, SW

Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines

Liu, T

Zhang, W

McLean, P

Ueland, M

Forbes, SL

Su, SW

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Publication Type:: Journal Article
Citation:: International Journal of Fuzzy Systems, 2018, 20 (4), pp. 1309 - 1320
Issue Date:: 2018-04-01

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Field	Value	Language
dc.contributor.author	Liu, T https://orcid.org/0000-0003-3827-7489	en_US
dc.contributor.author	Zhang, W https://orcid.org/0000-0002-2064-0326	en_US
dc.contributor.author	McLean, P https://orcid.org/0000-0002-7731-4927	en_US
dc.contributor.author	Ueland, M https://orcid.org/0000-0002-9155-3502	en_US
dc.contributor.author	Forbes, SL https://orcid.org/0000-0001-7416-0080	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2018-04-01	en_US
dc.identifier.citation	International Journal of Fuzzy Systems, 2018, 20 (4), pp. 1309 - 1320	en_US
dc.identifier.issn	1562-2479	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124369
dc.description.abstract	© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Electronic nose devices consisting of a matrix of sensors to sense the smell of various target gases have received considerable attention during the past two decades. This paper presents an efficient classification algorithm for a self-designed electronic nose, which integrates both genetic algorithms (GAs) and fuzzy support vector machines (FSVMs) to detect the target odor. GAs are applied to select the informative features and the optimal model parameters of FSVMs. FSVMs are adopted as fitness evaluation criterion and the sequent odor classifier, which can reduce the outlier effects and provide a robust and accurate classification. This proposed algorithm has been compared with some commonly used learning algorithms, such as support vector machine, the k-nearest neighbors and other combination algorithms. This study is based on experimental data collected from the response of the UTS NOS.E, which is the electronic nose system developed by the University of Technology Sydney NOS.E team. In comparison with other approaches, the experiment results show that the proposed odor classification algorithm can significantly improve the classification accuracy by selecting high-quality features and reach to 92.05% classification accuracy.	en_US
dc.relation.ispartof	International Journal of Fuzzy Systems	en_US
dc.relation.isbasedon	10.1007/s40815-018-0449-8	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	20	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CFS - Centre for Forensic Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Electronic nose devices consisting of a matrix of sensors to sense the smell of various target gases have received considerable attention during the past two decades. This paper presents an efficient classification algorithm for a self-designed electronic nose, which integrates both genetic algorithms (GAs) and fuzzy support vector machines (FSVMs) to detect the target odor. GAs are applied to select the informative features and the optimal model parameters of FSVMs. FSVMs are adopted as fitness evaluation criterion and the sequent odor classifier, which can reduce the outlier effects and provide a robust and accurate classification. This proposed algorithm has been compared with some commonly used learning algorithms, such as support vector machine, the k-nearest neighbors and other combination algorithms. This study is based on experimental data collected from the response of the UTS NOS.E, which is the electronic nose system developed by the University of Technology Sydney NOS.E team. In comparison with other approaches, the experiment results show that the proposed odor classification algorithm can significantly improve the classification accuracy by selecting high-quality features and reach to 92.05% classification accuracy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/124369