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Improving the Stability of Variable Selection for Industrial Datasets

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Neural Advances in Processing Nonlinear Dynamic Signals (WIRN 2017 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 102))

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Abstract

Variable reduction is an essential step in data mining, which is able effectively to increase both the performance of machine learning and the process knowledge by removing the redundant and irrelevant input variables. The paper presents a variable selection approach merging the dominating set procedure for redundancy analysis and a wrapper approach in order to achieve an informative and not redundant subset of variables improving both the stability and the computational complexity. The proposed approach is tested on different datasets coming from the UCI repository and from industrial contexts and is compared to the exhaustive variable selection approach, which is often considered optimal in terms of system performance. Moreover the novel method is applied to both classification and regression procedures.

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Acknowledgements

The work presented in this paper was developed within the project entitled “Piattaforma Integrata Avanzata per la Progettazione di Macchine e Sistemi Complessi” (PROMAS), which was co-funded under Tuscany POR FESR 2014–2020.

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Authors and Affiliations

  1. Scuola Superiore Sant’ Anna - TeCIP Institute, Via Alamanni 13B, 56010, Pisa, Italy

    Silvia Cateni, Valentina Colla & Vincenzo Iannino

Authors
  1. Silvia Cateni
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  2. Valentina Colla
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  3. Vincenzo Iannino
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Corresponding author

Correspondence to Silvia Cateni .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia, Università della Campana Luigi Vanvitelli, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataro, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Laboratorio di Neuronica, Dipartimento Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Cateni, S., Colla, V., Iannino, V. (2019). Improving the Stability of Variable Selection for Industrial Datasets. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_19

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