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An Efficient Algorithm for Inference in Rough Set Flow Graphs

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Transactions on Rough Sets V

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4100))

Abstract

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.

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References

  1. Beeri, C., Fagin, R., Maier, D., Yannakakis, M.: On The Desirability of Acyclic Database Schemes. Journal of the ACM 30(3), 479–513 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  2. Butz, C.J., Yan, W., Yang, B.: The computational complexity of inference using rough set flow graphs. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 335–344. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Dawid, A.P., Lauritzen, S.L.: Hyper Markov Laws in The Statistical Analysis of Decomposable Graphical Models. The Annals of Satistics 21, 1272–1317 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  4. Greco, S., Pawlak, Z., Słowiński, R.: Generalized decision algorithms, rough inference rules, and flow graphs. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 93–104. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Hajek, P., Havranek, T., Jirousek, R.: Uncertain Information Processing in Expert System (1992)

    Google Scholar 

  6. Madson, A.L., Jensen, F.V.: Lazy Propagation: A Junction Tree Inference Algorithm based on Lazy Evaluation. Artificial Intelligence 113(1-2), 203–245 (1999)

    Article  MathSciNet  Google Scholar 

  7. Pawlak, Z.: Flow Graphs and Decision Algorithms. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS, vol. 2639, Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Pawlak, Z.: In pursuit of patterns in data reasoning from data - the rough set way. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 1–9. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  9. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  10. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)

    MATH  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 

  13. Wong, S.K.M., Butz, C.J., Wu, D.: On the Implication Problem for Probabilistic Conditional Independency. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 30(6), 785–805 (2000)

    Article  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Butz, C.J., Yan, W., Yang, B. (2006). An Efficient Algorithm for Inference in Rough Set Flow Graphs. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_5

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  • DOI: https://doi.org/10.1007/11847465_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39382-5

  • Online ISBN: 978-3-540-39383-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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