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Sensors 2016, 16(9), 1462; doi:10.3390/s16091462

A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 16 July 2016 / Revised: 2 September 2016 / Accepted: 5 September 2016 / Published: 10 September 2016
(This article belongs to the Section Physical Sensors)
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Abstract

Electronic nose (E-nose), as a device intended to detect odors or flavors, has been widely used in many fields. Many labeled samples are needed to gain an ideal E-nose classification model. However, the labeled samples are not easy to obtain and there are some cases where the gas samples in the real world are complex and unlabeled. As a result, it is necessary to make an E-nose that cannot only classify unlabeled samples, but also use these samples to modify its classification model. In this paper, we first introduce a semi-supervised learning algorithm called S4VMs and improve its use within a multi-classification algorithm to classify the samples for an E-nose. Then, we enhance its performance by adding the unlabeled samples that it has classified to modify its model and by using an optimization algorithm called quantum-behaved particle swarm optimization (QPSO) to find the optimal parameters for classification. The results of comparing this with other semi-supervised learning algorithms show that our multi-classification algorithm performs well in the classification system of an E-nose after learning from unlabeled samples. View Full-Text
Keywords: electronic nose; semi-supervised learning; multi-classification; S4VMs electronic nose; semi-supervised learning; multi-classification; S4VMs
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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

Huang, T.; Jia, P.; He, P.; Duan, S.; Yan, J.; Wang, L. A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs. Sensors 2016, 16, 1462.

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