Cargando…

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selecti...

Descripción completa

Detalles Bibliográficos
Autores principales: Deng, Changjian, Lv, Kun, Shi, Debo, Yang, Bo, Yu, Song, He, Zhiyi, Yan, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021920/
https://www.ncbi.nlm.nih.gov/pubmed/29895771
http://dx.doi.org/10.3390/s18061909
_version_ 1783335567108341760
author Deng, Changjian
Lv, Kun
Shi, Debo
Yang, Bo
Yu, Song
He, Zhiyi
Yan, Jia
author_facet Deng, Changjian
Lv, Kun
Shi, Debo
Yang, Bo
Yu, Song
He, Zhiyi
Yan, Jia
author_sort Deng, Changjian
collection PubMed
description In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.
format Online
Article
Text
id pubmed-6021920
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-60219202018-07-02 Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework Deng, Changjian Lv, Kun Shi, Debo Yang, Bo Yu, Song He, Zhiyi Yan, Jia Sensors (Basel) Article In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect. MDPI 2018-06-12 /pmc/articles/PMC6021920/ /pubmed/29895771 http://dx.doi.org/10.3390/s18061909 Text en © 2018 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
Deng, Changjian
Lv, Kun
Shi, Debo
Yang, Bo
Yu, Song
He, Zhiyi
Yan, Jia
Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework
title Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework
title_full Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework
title_fullStr Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework
title_full_unstemmed Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework
title_short Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework
title_sort enhancing the discrimination ability of a gas sensor array based on a novel feature selection and fusion framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021920/
https://www.ncbi.nlm.nih.gov/pubmed/29895771
http://dx.doi.org/10.3390/s18061909
work_keys_str_mv AT dengchangjian enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework
AT lvkun enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework
AT shidebo enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework
AT yangbo enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework
AT yusong enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework
AT hezhiyi enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework
AT yanjia enhancingthediscriminationabilityofagassensorarraybasedonanovelfeatureselectionandfusionframework