Gas classification using binary decision tree classifier

Muhammad Hassan, Amine Bermak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Gas classification with an array of sensors is challenging for real life applications due to the limited amount of available training data of gases. Different pattern recognition algorithms are successfully used for gases identification, but their performance is degraded when the training and testing of these algorithms is done with different concentrations data. In this paper, we are using a binary decision tree approach for gas classification, and we are considering difference in the sensitivities of the sensors in every pair of a multi-sensor array as an input attribute for the tree. Suitable pairs of sensors are found by exploring their capability to split the available gases data samples at the decision node of the tree into two branches. A distance metric is used to select a single sensor pair in the case of more than one pair of sensors for the gases distribution at the decision node. The selected pairs of sensors learned during the training phase at the decision nodes are applied on the test data vectors. The effectiveness of our algorithm is successfully verified on the acquired data set with an array of seven metal oxide gas sensors for five different gases.

Original languageEnglish
Title of host publication2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2579-2582
Number of pages4
ISBN (Print)9781479934324
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 - Melbourne, VIC, Australia
Duration: 1 Jun 20145 Jun 2014

Other

Other2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014
CountryAustralia
CityMelbourne, VIC
Period1/6/145/6/14

Fingerprint

Decision trees
Classifiers
Gases
Sensors
Sensor arrays
Chemical sensors
Pattern recognition
Oxides
Testing
Metals

Keywords

  • binary decision tree
  • distance metric
  • gas classification
  • gas concentration
  • multi-sensors array

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Hassan, M., & Bermak, A. (2014). Gas classification using binary decision tree classifier. In 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014 (pp. 2579-2582). [6865700] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2014.6865700

Gas classification using binary decision tree classifier. / Hassan, Muhammad; Bermak, Amine.

2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2579-2582 6865700.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hassan, M & Bermak, A 2014, Gas classification using binary decision tree classifier. in 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014., 6865700, Institute of Electrical and Electronics Engineers Inc., pp. 2579-2582, 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014, Melbourne, VIC, Australia, 1/6/14. https://doi.org/10.1109/ISCAS.2014.6865700
Hassan M, Bermak A. Gas classification using binary decision tree classifier. In 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 2579-2582. 6865700 https://doi.org/10.1109/ISCAS.2014.6865700
Hassan, Muhammad ; Bermak, Amine. / Gas classification using binary decision tree classifier. 2014 IEEE International Symposium on Circuits and Systems, ISCAS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 2579-2582
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