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A Meta-Learning Approach to Methane Concentration Value Prediction

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

Abstract

A meta-learning approach to stream data analysis is presented in this work. The analysis is based on prediction of methane concentration in a coal mine. The results of the analysis show that the chosen approach achieves relatively low error values. Additionally, the impact of a data window size on a learning speed and quality was verified. The analysis is performed on a stream of measurements that was generated on a basis of real values collected in a coal mine.

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Acknowledgements

This research was supported by the Polish National Centre for Research and Development (NCBiR) grant PBS2/B9/20/2013 in the frame of the Applied Research Programme.

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Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, Ul. Akademicka 16, 44-100, Gliwice, Poland

    Michał Kozielski

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  1. Michał Kozielski
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Correspondence to Michał Kozielski .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Kozielski, M. (2016). A Meta-Learning Approach to Methane Concentration Value Prediction. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_56

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_56

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  • Online ISBN: 978-3-319-34099-9

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