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Window-Based Feature Engineering for Prediction of Methane Threats in Coal Mines

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

We present our results of experiments concerning the methane threats prediction in coal mines obtained during IJCRS’15 Data Challenge. The data mining competition task poses the problem of active monitoring and early threats detection which is essential to prevent spontaneous gas explosions. This issue is very important for the safety of people and equipment as well as minimization of production losses. The discussed research was conducted also to verify the effectiveness of the feature engineering framework developed in the DISESOR project. The utilized framework is based on a sliding window approach and is designed to handle numerous streams of sensor readings.

Partially supported by Polish National Science Centre - grant DEC-2012/05/B/ST6/03215 and by the Polish National Centre for Research and Development (NCBiR) – grant PBS2/B9/20/2013.

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Notes

  1. 1.

    https://knowledgepit.fedcsis.org/contest/view.php?id=109.

  2. 2.

    See https://knowledgepit.fedcsis.org/.

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Authors and Affiliations

  1. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Marek Grzegorowski & Sebastian Stawicki

Authors
  1. Marek Grzegorowski
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  2. Sebastian Stawicki
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Corresponding author

Correspondence to Marek Grzegorowski .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Grzegorowski, M., Stawicki, S. (2015). Window-Based Feature Engineering for Prediction of Methane Threats in Coal Mines. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_40

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

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