Volume 3 Supplement 1

Proceedings of the 1st International Workshop on Odor Spaces

Open Access

Feature selection in Enose applications

  • Thomas Nowotny1, 2,
  • Amalia Z Berna2,
  • Russell Binions3,
  • X Rosalind Wang4,
  • Joseph T Lizier4,
  • Mikhail Prokopenko4 and
  • Stephen Trowell2
Flavour20143(Suppl 1):O15

DOI: 10.1186/2044-7248-3-S1-O15

Published: 16 April 2014

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
(2)
CSIRO Ecosystem Sciences and Food Futures Flagship
(3)
School of Engineering and Materials Science, Queen Mary University of London
(4)
CSIRO Information and Communications Technologies Centre

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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