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Optimal feature selection for classifying a large set of chemicals using metal oxide sensors

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posted on 2023-06-08, 16:48 authored by Thomas NowotnyThomas Nowotny, Amalia Z Berna, Russell Binions, Stephen Trowell
Using linear support vector machines, we investigated the feature selection problem for the application of all-against-all classification of a set of 20 chemicals using two types of sensors, classical doped tin oxide and zeolite-coated chromium titanium oxide sensors. We defined a simple set of possible features, namely the identity of the sensors and the sampling times and tested all possible combinations of such features in a wrapper approach. We confirmed that performance is improved, relative to previous results using this data set, by exhaustive comparison of these feature sets. Using the maximal number of different sensors and all available data points for each sensor does not necessarily yield the best results, even for the large number of classes in this problem. We contrast this analysis, using exhaustive screening of simple feature sets, with a number of more complex feature choices and find that subsampled sets of simple features can perform better. Analysis of potential predictors of classification performance revealed some relevance of clustering properties of the data and of correlations among sensor responses but failed to identify a single measure to predict classification success, reinforcing the relevance of the wrapper approach used. Comparison of the two sensor technologies showed that, in isolation, the doped tin oxide sensors performed better than the zeolite-coated chromium titanium oxide sensors but that mixed arrays, combining both technologies, performed best.

Funding

OCE Distinguished Visiting Scientist; CSIRO

History

Publication status

  • Published

File Version

  • Published version

Journal

Sensors and Actuators B: Chemical

ISSN

0925-4005

Publisher

Elsevier

Volume

187

Page range

471-480

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2014-03-03

First Open Access (FOA) Date

2014-03-03

First Compliant Deposit (FCD) Date

2014-03-03

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