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Breath Analysis for Medical Applications pp 181–206Cite as

Feature Selection and Analysis on Correlated Breath Data

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Abstract

Feature selection is a useful step in data analysis procedure. In this chapter, we study the classical support vector machine recursive feature elimination (SVM-RFE) algorithm and improve it by incorporating a correlation bias reduction (CBR) strategy into the feature elimination procedure. Experiments are conducted on a synthetic dataset and two breath analysis datasets. Large and comprehensive sets of transient features are extracted from the sensor responses. The classification accuracy with feature selection proves the efficacy of the proposed SVM-RFE + CBR. It outperforms the original SVM-RFE and other typical algorithms. An ensemble method is further studied to improve the stability of the proposed method. By statistically analyzing the features’ rankings, some knowledge is obtained, which can guide future design of e-noses and feature extraction algorithms.

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Authors and Affiliations

  1. Biometrics Research Centre, The Hong Kong Polytechnic University, Hong Kong, China

    David Zhang

  2. Wake Forest University, Winston-Salem, NC, USA

    Dongmin Guo

  3. National Institute of Health, Bethesda, USA

    Ke Yan

Authors
  1. David Zhang
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  2. Dongmin Guo
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  3. Ke Yan
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Correspondence to David Zhang .

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© 2017 Springer Nature Singapore Pte Ltd.

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Zhang, D., Guo, D., Yan, K. (2017). Feature Selection and Analysis on Correlated Breath Data . In: Breath Analysis for Medical Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-4322-2_10

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  • DOI: https://doi.org/10.1007/978-981-10-4322-2_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4321-5

  • Online ISBN: 978-981-10-4322-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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