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Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines

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Abstract

Electronic nose devices consisting of a matrix of sensors to sense the smell of various target gases have received considerable attention during the past two decades. This paper presents an efficient classification algorithm for a self-designed electronic nose, which integrates both genetic algorithms (GAs) and fuzzy support vector machines (FSVMs) to detect the target odor. GAs are applied to select the informative features and the optimal model parameters of FSVMs. FSVMs are adopted as fitness evaluation criterion and the sequent odor classifier, which can reduce the outlier effects and provide a robust and accurate classification. This proposed algorithm has been compared with some commonly used learning algorithms, such as support vector machine, the k-nearest neighbors and other combination algorithms. This study is based on experimental data collected from the response of the UTS NOS.E, which is the electronic nose system developed by the University of Technology Sydney NOS.E team. In comparison with other approaches, the experiment results show that the proposed odor classification algorithm can significantly improve the classification accuracy by selecting high-quality features and reach to 92.05% classification accuracy.

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Author notes

  1. T. Liu, W. Zhang: These authors contributed equally to this work.

Authors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Taoping Liu, Wentian Zhang, Peter McLean & Steven W. Su

  2. School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Maiken Ueland & Shari L. Forbes

Authors
  1. Taoping Liu
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  2. Wentian Zhang
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  3. Peter McLean
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  4. Maiken Ueland
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  5. Shari L. Forbes
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  6. Steven W. Su
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Corresponding author

Correspondence to Steven W. Su.

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Liu, T., Zhang, W., McLean, P. et al. Electronic Nose-Based Odor Classification using Genetic Algorithms and Fuzzy Support Vector Machines. Int. J. Fuzzy Syst. 20, 1309–1320 (2018). https://doi.org/10.1007/s40815-018-0449-8

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  • DOI: https://doi.org/10.1007/s40815-018-0449-8

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