Regression Rule Learning for Methane Forecasting in Coal Mines

  • Michał Kozielski
  • Adam Skowron
  • Łukasz Wróbel
  • Marek Sikora
Conference paper

DOI: 10.1007/978-3-319-18422-7_44

Part of the Communications in Computer and Information Science book series (CCIS, volume 521)
Cite this paper as:
Kozielski M., Skowron A., Wróbel Ł., Sikora M. (2015) Regression Rule Learning for Methane Forecasting in Coal Mines. In: Kozielski S., Mrozek D., Kasprowski P., Małysiak-Mrozek B., Kostrzewa D. (eds) Beyond Databases, Architectures and Structures. BDAS 2015. Communications in Computer and Information Science, vol 521. Springer, Cham

Abstract

The rule-based approach to methane concentration prediction is presented in this paper. The applied solution is based on the modification called fixed of the separate-and-conquer rule induction approach. We also proposed the modification of a rule quality evaluation based on confidence intervals calculated for positive and negative examples covered by the rule. The characteristic feature of the considered methane forecasting model is that it omits the readings of the sensor being the subject of forecasting. The approach is evaluated on a real life data set acquired during a week in a coal mine. The results show the advantages of the introduced method (in terms of both the prediction accuracy and knowledge extraction) in comparison to the standard approaches typically implemented in the analytical tools.

Keywords

Prediction Rule-based regression Statistical rule quality evaluation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michał Kozielski
    • 1
  • Adam Skowron
    • 2
  • Łukasz Wróbel
    • 3
  • Marek Sikora
    • 2
  1. 1.Institute of ElectronicsSilesian University of TechnologyGliwicePoland
  2. 2.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  3. 3.Institute of Innovative TechnologiesEMAGKatowicePoland

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