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

Author： Huang Tailai Jia Pengfei He Peilin Duan Shukai Yan Jia Wang Lidan

Publisher： MDPI

E-ISSN： 1424-8220|16|9|1462-1462

ISSN： 1424-8220

Source： Sensors, Vol.16, Iss.9, 2016-09, pp. : 1462-1462

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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.