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

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

Abstract

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.

Keywords

Data stream mining Rule-based learning Classification 

Notes

Acknowledgements

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.

References

  1. 1.
    Almeida, E., Kosina, P., Gama, J.: Random rules from data streams. In: SAC 2013, Coimbra, Portugal, pp. 813–814 (2013)Google Scholar
  2. 2.
    An, A., Cercone, N.: Rule quality measures for rule induction systems: description and evaluation. Comput. Intell. 17(3), 409–424 (2001)CrossRefGoogle Scholar
  3. 3.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
  4. 4.
    Bruha, I., Tkadlec, J.: Rule quality for multiple-rule classifier: empirical expertise and theoretical methodology. Intell. Data Anal. 7(2), 99–124 (2003)MATHGoogle Scholar
  5. 5.
    Ferrer-Troyano, F.J., Aguilar-Ruiz, J.S., Santos, J.C.R.: Incremental rule learning and border examples selection from numerical data streams. J. Univ. Comput. Sci. 11(8), 1426–1439 (2005)Google Scholar
  6. 6.
    Gama, J., Kosina, P.: Learning decision rules from data streams. In: IJCAI 2011, Barcelona, Spain, pp. 1255–1260 (2011)Google Scholar
  7. 7.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9 (2006)CrossRefGoogle Scholar
  8. 8.
    Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Disc. 23(1), 128–168 (2011)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Janssen, F., Fürnkranz, J.: On the quest for optimal rule learning heuristics. Mach. Learn. 78, 343–379 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kosina, P., Gama, J.: Very fast decision rules for multi-class problems. In: SAC 2012, Trento, Italy, pp. 795–800 (2012)Google Scholar
  11. 11.
    Kosina, P., Gama, J.: Very fast decision rules for classification in data streams. Data Min. Knowl. Disc. 29(1), 168–202 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kozielski, M., Sikora, M., Wróbel, Ł.: Decision support and maintenance system for natural hazards, processes and equipment monitoring. Eksploatacja i Niezawodność-Maint. Reliab. 18(2), 218–228 (2016)CrossRefGoogle Scholar
  13. 13.
    Maloof, M.A., Michalski, R.S.: Incremental learning with partial instance memory. Artif. Intell. 154(1), 95–126 (2004)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Nguyen, H.L., Woon, Y.K., Ng, W.K.: A survey on data stream clustering and classification. Knowl. Inf. Syst. 45(3), 535–569 (2015)CrossRefGoogle Scholar
  15. 15.
    Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)CrossRefGoogle Scholar
  16. 16.
    Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Mach. Learn. 1(3), 317–354 (1986)Google Scholar
  17. 17.
    Sikora, M., Wróbel, Ł.: Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm. In: Kuznetsov, S.O., Ślȩzak, D., Hepting, D.H., Mirkin, B.G. (eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, 25–27 June 2011. LNCS, vol. 6743, pp. 278–285. Springer, Heidelberg (2011)Google Scholar
  18. 18.
    Sikora, M., Wróbel, L.: Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms. Int. J. Gen Syst 42(6), 594–613 (2013)CrossRefGoogle Scholar
  19. 19.
    Slezak, D., Ziarko, W.: The investigation of the Bayesian rough set model. Int. J. Approx. Reason. 40(1), 81–91 (2005)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)Google Scholar
  21. 21.
    Xiong, H., Shekhar, S., Tan, P.N., Kumar, V.: Exploiting a support-based upper bound of pearson’s correlation coefficient for efficiently identifying strongly correlated pairs. In: SIGKDD 2004, Seattle, USA, pp. 334–343 (2004)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Michał Kozielski
    • 1
  • 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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