The Computational Complexity of Inference Using Rough Set Flow Graphs

  • Cory J. Butz
  • Wen Yan
  • Boting Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3641)


Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reasoning from data. Each rule is associated with three coefficients, which have been shown to satisfy Bayes’ theorem. Thereby, RSFGs provide a new perspective on Bayesian inference methodology.

In this paper, we show that inference in RSFGs takes polynomial time with respect to the largest domain of the variables in the decision tables. Thereby, RSFGs provide an efficient tool for uncertainty management. On the other hand, our analysis also indicates that a RSFG is a special case of conventional Bayesian network and that RSFGs make implicit assumptions regarding the problem domain.


Bayesian Network Directed Acyclic Graph Problem Domain Joint Probability Distribution Decision Table 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Cory J. Butz
    • 1
  • Wen Yan
    • 1
  • Boting Yang
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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