A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs
AbstractElectronic 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
Share & Cite This Article
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.
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(9):1462.Chicago/Turabian Style
Huang, Tailai; Jia, Pengfei; He, Peilin; Duan, Shukai; Yan, Jia; Wang, Lidan. 2016. "A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs." Sensors 16, no. 9: 1462.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.