Decision Rule Learning from Stream of Measurements—A Case Study in Methane Hazard Forecasting in Coal Mines

  • Michał KozielskiEmail author
  • Paweł Matyszok
  • Marek Sikora
  • Łukasz Wróbel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 659)


The approach based on the Very Fast Decision Rules algorithm in application to prediction of alarm state resulting from methane hazard in coal mines is presented in this work. The approach introduces the modification of rule induction process due to application of the Correlation rule quality measure. An evaluation of the introduced method on a real life stream data collected from coal mine sensors is performed. The results show advantages of the introduced method considering both the classification quality and the rule-based knowledge representation.


Data stream mining Rule-based learning Classification 



The work was carried out within the statutory research projects of the Institute of Electronics, Silesian University of Technology (BK_220 /RAu-3/2016 (02/030/BK_16/0017)) and the statutory research fund of the Institute of Innovative Technologies EMAG.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Michał Kozielski
    • 1
    Email author
  • Paweł Matyszok
    • 2
  • Marek Sikora
    • 2
    • 3
  • Łukasz Wróbel
    • 2
    • 3
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland
  2. 2.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  3. 3.Institute of Innovative Technologies EMAGKatowicePoland

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