A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs.
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.
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. 18580465830@163.com.
Tailai
T
Huang
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. jiapengfei200609@126.com.
Pengfei
P
Jia
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. qaz321123@email.swu.edu.cn.
Peilin
P
He
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. duansk@swu.edu.cn.
Shukai
S
Duan
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. yanjia119@163.com.
Jia
J
Yan
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. ldwang@swu.edu.cn.
Lidan
L
Wang
D016902:Air Pollution, Indoor / Q000032:analysis*; D019992:Databases as Topic; D062609:Electronic Nose; D060388:Support Vector Machine
S4VMs; electronic nose; multi-classification; semi-supervised learning