Building parsimonious fuzzy ARTMAP models by variable selection with a cascaded genetic algorithm: application to multisensor systems for gas analysis

Considerable attention has been given to strategies for variable selection in gas analysis using multisensor systems. Of the stochastic methods, genetic algorithms (GAs) have been found to be useful for variable selection, although they do not prevent irrelevant variables from being selected. Here w...

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Bibliographic Details
Published in:Sensors and actuators. B, Chemical Chemical, 2004-05, Vol.99 (2), p.267-272
Main Authors: Llobet, Eduard, Brezmes, Jesús, Gualdrón, Oscar, Vilanova, Xavier, Correig, Xavier
Format: Article
Language:English
Subjects:
Fuzzy ARTMAP
Gas sensor array
Genetic algorithm
Variable selection
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Summary:Considerable attention has been given to strategies for variable selection in gas analysis using multisensor systems. Of the stochastic methods, genetic algorithms (GAs) have been found to be useful for variable selection, although they do not prevent irrelevant variables from being selected. Here we introduce the cascaded genetic algorithm, a new and simple approach that removes initially selected irrelevant variables. We coupled the method to the fuzzy ARTMAP classifier and used it to analyze single vapors and binary mixtures of three volatile organic compounds using a 12-element metal oxide gas sensor array. The method allowed to reduce the number of variables used to build the classifiers from 120 down to nine, which significantly increased the generalization ability of the fuzzy ARTMAP classifier. Success rates of 91.67 and 88.33% were reached in the simultaneous identification, quantification of single vapors, and in the identification of single vapors and their binary mixtures, respectively.
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2003.11.019