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  • Oral presentation
  • Open Access

Feature selection in Enose applications

  • 1, 2,
  • 2,
  • 3,
  • 4,
  • 4,
  • 4 and
  • 2
Flavour20143 (Suppl 1) :O15

https://doi.org/10.1186/2044-7248-3-S1-O15

  • Published:

Keywords

  • Public Health
  • Physical Chemistry
  • Classification System
  • Feature Selection
  • Research Area

In my presentation I will summarise results for feature selection from a number of Enose applications ranging from general chemical classification and breath analysis with metal-oxide based Enoses to work scoping the use of biological receptors, in particular receptors of the fruit fly Drosophila, for applications in wine making and explosives detection. The common thread in all applications is the availability of high-dimensional data which is often noisy and in all of its details not necessarily very information rich for any particular application. The challenge for building useful classification systems is to define, extract and select the most "informative" features from the high-dimensional data. I will give an overview over our work using exhaustive "wrapper" approaches and a brief comparison to information-theory based methods. I will conclude by highlighting the most pertinent open questions encountered in this research area.

Authors’ Affiliations

(1)
CCNR, School of Engineering and Informatics, University of Sussex, Brighton BN1 9QJ, Falmer, UK
(2)
CSIRO Ecosystem Sciences and Food Futures Flagship, GPO Box 1700 Canberra,, ACT 2601,, Australia
(3)
School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK
(4)
CSIRO Information and Communications Technologies Centre, PO Box 76,, 1710 Epping, NSW, Australia

Copyright

© Nowotny et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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