NetTRS – Induction and Postprocessing of Decision Rules

  • Marek Sikora
  • Marcin Michalak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


The internet service NetTRS that enable to induction, evaluation, and postprocessing of decision rules is presented in the paper. The TRS library is the main part of the service. The TRS library makes possible, among others, induction of decision rules by means of tolerance rough sets model.


Decision Rule Rule Induction Decision Class Conditional Attribute NetTRS Induction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    An, A., Cercone, N.: Rule quality measures for rule induction systems – description and evaluation. Computational Intelligence 17(3), 409–424 (2001)CrossRefGoogle Scholar
  2. 2.
    Bazan, J., Szczuka, M., Wróblewski, J.: A new version of rough set exploration system. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 14–16. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Bazan, J.G., Latkowski, R., Szczuka, M.S.: DIXER – distributed executor for rough set exploration system. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 39–47. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Bruha, I.: Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules. In: Nakhaeizadeh, G., Taylor, C.C. (eds.) Machine Learning and Statistics, The Interface. John Wiley and Sons, Chichester (1997)Google Scholar
  5. 5.
    Nguyen, H.S., Nguyen, S.H.: Some Efficient Algorithms for Rough Set Methods. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, vol. 2, pp. 1451–1456 (1996)Google Scholar
  6. 6.
    Pawlak, Z.: Rough Sets. Theoretical aspects of reasoning about data. Kluwer, Dordrecht (1991)Google Scholar
  7. 7.
    Sikora, M.: An algorithm for generalization of decision rules by joining. Foundation on Computing and Decision Sciences 30(3) (2005)Google Scholar
  8. 8.
    Sikora, M.: Approximate decision rules induction algorithm using rough sets and rule-related quality measures. Theoretical and Applied Informatics (4) (2004)Google Scholar
  9. 9.
    Sikora, M., Proksa, P.: Algorithms for generation and filtration of approximate decision rules, using rule-related quality measures. In: Bulletin of International Rough Set Society (RSTGC 2001), vol. 5(1/2) (2001)Google Scholar
  10. 10.
    Sikora, M.: Filtering of decision rules sets using rules quality measures. Studia Informatica, Gliwice 46(4) (2001)Google Scholar
  11. 11.
    Sikora, M., Proksa, P.: Induction of decision and association rules for knowledge discovery in industrial databases. In: ICDM 2004, Alternative Techniques for Data Mining Workshop, UK, November 1-4 (2004)Google Scholar
  12. 12.
    Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)MATHMathSciNetGoogle Scholar
  13. 13.
    Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information systems. In: Sowiski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer, Dordrecht (1992)Google Scholar
  14. 14.
    Stefanowski, J.: Rough set based rule induction techniques for classification problems. In: Proc. 6th European Congress of Intelligent Techniques and Soft Computing, Achen, September 7-10, vol. 1, pp. 107–119 (1998)Google Scholar
  15. 15.
    Stepaniuk, J.: Knowledge Discovery by Application of Rough Set Models. Institute of Compuer Sciences Polish Academy of Sciences, Reports, no. 887, Warszawa (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marek Sikora
    • 1
  • Marcin Michalak
    • 1
  1. 1.Institute of Computer SciencesSilesian University of TechnologyGliwicePoland

Personalised recommendations