title: A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training.
abstract: When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde). Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training) is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. jiapengfei200609@126.com.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Jia
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. 18580465830@163.com.
authors.forename: Tailai
authors.initials: T
authors.lastname: Huang
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. duansk@swu.edu.cn.
authors.forename: Shukai
authors.initials: S
authors.lastname: Duan
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. gelingpu@126.com.
authors.forename: Lingpu
authors.initials: L
authors.lastname: Ge
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. yanjia119@163.com.
authors.forename: Jia
authors.initials: J
authors.lastname: Yan
authors.affiliation: College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China. ldwang@swu.edu.cn.
authors.forename: Lidan
authors.initials: L
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001554:Benzene / Q000302:isolation & purification*; D015374:Biosensing Techniques / Q000295:instrumentation*; D062609:Electronic Nose; D005557:Formaldehyde / Q000302:isolation & purification*; D006801:Humans; D014050:Toluene / Q000302:isolation & purification*
chemical_list: D005557:Formaldehyde; D014050:Toluene; D001554:Benzene
keywords: electronic nose; indoor pollution gas; semi-supervised learning; unlabeled samples