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Sensors 2016, 16(7), 1088; doi:10.3390/s16071088

Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose

1
State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China
2
Computer Learning Research Centre, Royal Holloway, University of London, Egham Hill, Egham, Surrey TW20 0EX, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Carmen Horrillo Güemes
Received: 18 April 2016 / Revised: 22 June 2016 / Accepted: 22 June 2016 / Published: 13 July 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
View Full-Text   |   Download PDF [2016 KB, uploaded 13 July 2016]   |  

Abstract

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. View Full-Text
Keywords: electronic nose; ginseng; Venn machine; probabilistic prediction; support vector machine electronic nose; ginseng; Venn machine; probabilistic prediction; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, Y.; Miao, J.; Lyu, X.; Liu, L.; Luo, Z.; Li, G. Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose. Sensors 2016, 16, 1088.

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