An Efficient Algorithm for Inference in Rough Set Flow Graphs
Pawlak recently introduced rough set flow graphs (RSFGs) as a graphical framework for reasoning from data. No study, however, has yet investigated the complexity of the accompanying inference algorithm, nor the complexity of inference in RSFGs. In this paper, we show that the traditional RSFG inference algorithm has exponential time complexity. We then propose a new RSFG inference algorithm that exploits the factorization in a RSFG. We prove its correctness and establish its polynomial time complexity. In addition, we show that our inference algorithm never does more work than the traditional algorithm. Our discussion also reveals that, unlike traditional rough set research, RSFGs make implicit independency assumptions regarding the problem domain.
KeywordsReasoning under uncertainty rough set flow graphs
Unable to display preview. Download preview PDF.
- 5.Hajek, P., Havranek, T., Jirousek, R.: Uncertain Information Processing in Expert System (1992)Google Scholar
- 11.Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
- 12.Shafer, G.: Probabilistic Expert Systems. Society for the Institute and Applied Mathematics, Philadelphia (1996)Google Scholar