Next Article in Journal
Particle Filter with Novel Nonlinear Error Model for Miniature Gyroscope-Based Measurement While Drilling Navigation
Next Article in Special Issue
Identification of a Large Pool of Microorganisms with an Array of Porphyrin Based Gas Sensors
Previous Article in Journal
Development of Mobile Mapping System for 3D Road Asset Inventory
Previous Article in Special Issue
Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(3), 370;

A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
Author to whom correspondence should be addressed.
Academic Editor: M. Carmen Horrillo Güemes
Received: 1 February 2016 / Revised: 7 March 2016 / Accepted: 9 March 2016 / Published: 14 March 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
PDF [1947 KB, uploaded 15 March 2016]


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. View Full-Text
Keywords: electronic nose; semi-supervised learning; unlabeled samples; indoor pollution gas electronic nose; semi-supervised learning; unlabeled samples; indoor pollution gas

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Jia, P.; Huang, T.; Duan, S.; Ge, L.; Yan, J.; Wang, L. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training. Sensors 2016, 16, 370.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top