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Machine Learning Techniques to Select a Reduced and Optimal Set of Sensors for the Design of Ad Hoc Sensory Systems

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Sensors (CNS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 539))

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Abstract

The first step of this research has been to discriminate, by means of a commercial electronic nose (e-nose), the maturity evolution of seven types of fruits stored in refrigerated cells, from the post-harvest period till the beginning of the marcescence. The final aim was to determine a procedure to select a reduced set of sensors that can be efficiently used to monitor the same class of fruits by a low cost system with few, suitable sensors without loss in accuracy and generalization. To define the best subset we have compared the use of a projection technique (the Principal Component Analysis, PCA) with the sequential feature selection technique (Sequential Forward Selection, SFS) and the Genetic Algorithm (GA) technique by using classification schemes like Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (kNN) and applying two data pre-processing methods. We have determined a subset of only three sensors which gives a classification accuracy near 100%. This procedure can be generalized to other experimental situations to select a minimal and optimal set of sensors to be used in consumer applications for the design of ad hoc sensory systems.

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Acknowledgements

This work has been partially funded by Italian Ministry of Economic Development thanks to “Ricerca di Sistema elettrico”, “ORTOFRULOG”, and “Magazzino Viag-giante” projects. The authors are thankful to Paolo di Lorenzo for his support in the experimental campaigns.

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Authors and Affiliations

  1. ENEA, Casaccia Research Centre, Via Anguillarese 301, 00123, Rome, Italy

    Luigi Quercia & Domenico Palumbo

Authors
  1. Luigi Quercia
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  2. Domenico Palumbo
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Corresponding author

Correspondence to Luigi Quercia .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Bruno Andò

  2. IFAC-CNR, Sesto Fiorentino, Florence, Italy

    Francesco Baldini

  3. University of Rome Tor Vergata, Rome, Italy

    Corrado Di Natale

  4. Department of Information Engineering (DII), University of Brescia, Brescia, Italy

    Vittorio Ferrari

  5. University of Catania, Catania, Italy

    Vincenzo Marletta

  6. Department of Chemistry, University of Florence, Sesto Fiorentino, Florence, Italy

    Giovanna Marrazza

  7. University of Palermo, Palermo, Italy

    Valeria Militello

  8. University of Padova, Padua, Italy

    Giorgia Miolo

  9. Sapienza University of Rome, Rome, Italy

    Marco Rossi

  10. Università Politecnica delle Marche (UNIVPM), Ancona, Italy

    Lorenzo Scalise

  11. IMM-CNR, Lecce, Italy

    Pietro Siciliano

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Quercia, L., Palumbo, D. (2019). Machine Learning Techniques to Select a Reduced and Optimal Set of Sensors for the Design of Ad Hoc Sensory Systems. In: Andò, B., et al. Sensors. CNS 2018. Lecture Notes in Electrical Engineering, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-04324-7_50

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  • DOI: https://doi.org/10.1007/978-3-030-04324-7_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04323-0

  • Online ISBN: 978-3-030-04324-7

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