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Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different...

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Autores principales: Wang, You, Miao, Jiacheng, Lyu, Xiaofeng, Liu, Linfeng, Luo, Zhiyuan, Li, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970134/
https://www.ncbi.nlm.nih.gov/pubmed/27420074
http://dx.doi.org/10.3390/s16071088
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author Wang, You
Miao, Jiacheng
Lyu, Xiaofeng
Liu, Linfeng
Luo, Zhiyuan
Li, Guang
author_facet Wang, You
Miao, Jiacheng
Lyu, Xiaofeng
Liu, Linfeng
Luo, Zhiyuan
Li, Guang
author_sort Wang, You
collection PubMed
description In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt’s method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt’s method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.
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spelling pubmed-49701342016-08-04 Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose Wang, You Miao, Jiacheng Lyu, Xiaofeng Liu, Linfeng Luo, Zhiyuan Li, Guang Sensors (Basel) Article In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt’s method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt’s method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples. MDPI 2016-07-13 /pmc/articles/PMC4970134/ /pubmed/27420074 http://dx.doi.org/10.3390/s16071088 Text en © 2016 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
Wang, You
Miao, Jiacheng
Lyu, Xiaofeng
Liu, Linfeng
Luo, Zhiyuan
Li, Guang
Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
title Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
title_full Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
title_fullStr Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
title_full_unstemmed Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
title_short Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose
title_sort valid probabilistic predictions for ginseng with venn machines using electronic nose
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970134/
https://www.ncbi.nlm.nih.gov/pubmed/27420074
http://dx.doi.org/10.3390/s16071088
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