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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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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. |
format | Online Article Text |
id | pubmed-4970134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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