Classification with quantification for air quality monitoring

Al-Maskari, Sanad, Belisle, Eve, Li, Xue, Le Digabel, Sebastien, Nawahda, Amin and Zhong, Jiang (2016). Classification with quantification for air quality monitoring. In: James Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang and Ruili Wang, Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, Auckland, New Zealand, (578-590). 19 - 22 April 2016. doi:10.1007/978-3-319-31753-3_46


Author Al-Maskari, Sanad
Belisle, Eve
Li, Xue
Le Digabel, Sebastien
Nawahda, Amin
Zhong, Jiang
Title of paper Classification with quantification for air quality monitoring
Conference name 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Conference location Auckland, New Zealand
Conference dates 19 - 22 April 2016
Proceedings title Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2016
Sub-type Fully published paper
DOI 10.1007/978-3-319-31753-3_46
Open Access Status Not yet assessed
ISBN 9783319317526
9783319317533
ISSN 1611-3349
0302-9743
Editor James Bailey
Latifur Khan
Takashi Washio
Gillian Dobbie
Joshua Zhexue Huang
Ruili Wang
Volume 9651
Start page 578
End page 590
Total pages 13
Chapter number 46
Total chapters 47
Language eng
Abstract/Summary In this paper, a fuzzy classification with quantification algorithm is proposed for solving the air quality monitoring problem using e-noses. When e-noses are used in dynamic outdoor environment, the performance suffers from noise, signal drift and fast-changing natural environment. The question is, how to develop a prediction model capable of detecting as well as quantifying gases effectively and efficiently? The current research work has focused either on detection or quantification of sensor response without taking into account of dynamic factors. In this paper, we propose a new model, namely, Fuzzy Classification with Quantification Model (FCQM) to cope with the above mentioned challenges. To evaluate our model, we conducted extensive experiments on a publicly available datasets generated over a three-year period, and the results demonstrate its superiority over other baseline methods. To our knowledge, gas type detection together with quantification is an unsolved challenge. Our paper provides the first solution for this kind.
Keyword Classification
Clustering
Drift
E-nose
E-nose
Gaussian process regression
MOX
Noise
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 1 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 10 May 2016, 13:13:53 EST by System User on behalf of Learning and Research Services (UQ Library)