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International Workshop on Soft Computing Models in Industrial and Environmental Applications

International Conference on EUropean Transnational Education

Computational Intelligence in Security for Information Systems Conference

SOCO 2017, ICEUTE 2017, CISIS 2017: International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding pp 482–491Cite as

Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 649))

Abstract

Public health in developed countries is heavily affected by pollution specially in highly populated areas. Amongst the pollutants with greatest impact in health, ozone is particularly addressed in this paper due to importance of its effect on cardiovascular and respiratory problems and their prevalence on developed societies. Local authorities are compelled to provide satisfactory predictions of ozone levels and thus the need of proper estimation tools rises. A data driven approach to prediction demands high quality data but those observations collected by weather stations usually fail to meet this requirement. This paper reports a new approach to robust ozone levels prediction by using an outlier detection technique in an innovative way. The aim is to assess the feasibility of using raw data without preprocessing in order to obtain similar or better results than with traditional outlier removal techniques. An experimental dataset from a location in Spain, Ponferrada, is used through an experimental stage in which such approach provides satisfactory results in a difficult case.

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Acknowledgements

We gratefully acknowledge the financial support of Spanish Ministerio de Economía, Industria y Competitividad through grant DPI2016-79960-C3-2-P.

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Authors and Affiliations

  1. Universidad de León, 24071, León, Spain

    Hector Alaiz-Moreton, Laura Fernández-Robles, Javier Alfonso-Cendón, Manuel Castejón-Limas, Lidia Sánchez-González & Hilde Pérez

Authors
  1. Hector Alaiz-Moreton
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  2. Laura Fernández-Robles
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  3. Javier Alfonso-Cendón
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  4. Manuel Castejón-Limas
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  5. Lidia Sánchez-González
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  6. Hilde Pérez
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Corresponding author

Correspondence to Laura Fernández-Robles .

Editor information

Editors and Affiliations

  1. University of Leon, Leon, Spain

    Hilde Pérez García

  2. University of Leon, Leon, Spain

    Javier Alfonso-Cendón

  3. University of Leon, Leon, Spain

    Lidia Sánchez González

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Alaiz-Moreton, H., Fernández-Robles, L., Alfonso-Cendón, J., Castejón-Limas, M., Sánchez-González, L., Pérez, H. (2018). Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-67180-2_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67179-6

  • Online ISBN: 978-3-319-67180-2

  • eBook Packages: EngineeringEngineering (R0)

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