Skip to main content
SpringerLink
Log in

Machine Ranking of 2-Uncertain Rules Acquired from Real Data

  • Chapter
Transactions on Computational Collective Intelligence XI

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8065))

  • 511 Accesses

Abstract

There are many places (e.g. hospital emergency rooms) where reliable diagnostic systems might support people in their work. They could have form of RBSs with uncertainty and use the techniques of forward and backward chaining in their reasoning. The number and the contents of derived hypotheses depend then both on the form of the system’s knowledge base and on the inference engine performance. The paper provides detailed considerations on designing and applying particular uncertain rules, namely 2-uncertain rules. They are equipped with two reliability factors, representing a kind of second order probability. The rules can be acquired from real data of attributive representation. In the paper we propose a method for calculating the two reliability factors. We also suggest how to take advantage of the factors during reasoning, in order to obtain reliable hypotheses. The factors help to rank the rules and to fire them in the best order.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lenzerini, M.: Data Integration: A Theoretical Perspective. In: Popa, L. (ed.) Proc. 21st ACM Symposium on Principles of Database Systems, pp. 233–246. ACM, Madison (2002)

    Google Scholar 

  2. Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press (2001)

    Google Scholar 

  3. Rossman, A.J., Chance, B., von Oehsen, B.J.: Workshop Statistics: Discovery with Data and the Graphic Calculator, 3rd edn. John Wiley & Sons (2008)

    Google Scholar 

  4. Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data Exchange: Semantics and Query Answering. Theoretical Computer Science 336(1), 89–124 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Fagin, R., Kolaitis, P.G., Popa, L., Tan, W.C.: Schema Mapping Evolution through Composition and Inversion. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Schema Matching and Mapping, pp. 191–222. Springer (2011)

    Google Scholar 

  6. Gottlob, G., Koch, C., Pichler, R.: Efficient algorithms for processing XPath queries. In: Bernstein, P.A., Ioannidis, Y.E., Ramakrishnan, R., Papadias, D. (eds.) Proc. 28th Int. Conf. on Very Large Data Bases, pp. 95–106. Morgan Kaufmann, Hong Kong (2002)

    Chapter  Google Scholar 

  7. XML Path Language (XPath) 2.0, 2nd edn. (2010), http://www.w3.org/TR/xpath20/

  8. Ligęza, A.: Logical Foundations for Rule-Based Systems, 2nd edn. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  9. Beeri, C., Ramakrishnan, R.: On the Power of Magic. Journal of Logic Programming 10, 255–299 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  10. Agraval, R., Imielinski, T., Swani, A.: Mining association rules between sets of items in large databases. SIGMOD Record 22(2), 805–810 (1993)

    Google Scholar 

  11. Zadeh, L.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  12. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)

    Google Scholar 

  13. Bayes, T.: An Essay towards solving a Problem in the Doctrine of Chances. Philosophical Transactions of the Royal Society of London 53, 370–418 (1763)

    Google Scholar 

  14. Dempster, A.P.: A generalization of Bayesian inference. Journal of the Royal Statistical Society, Series B 30, 205–247 (1968)

    MathSciNet  MATH  Google Scholar 

  15. Barnett, J.A.: Computational methods for a mathematical theory of evidence. In: Proc. 7th Int. Joint Conf. on Artificial Intelligence, Vancouver, pp. 868–875 (1981)

    Google Scholar 

  16. Shortliffe, E.H.: Computer-Based Medical Consultations: MYCIN. Elsevier/North Holland, New York (1976)

    Google Scholar 

  17. Buchanan, B.G., Shortliffe, E.H.: Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison Wesley, Reading (1984)

    Google Scholar 

  18. Van der Gaag, L.C.: A conceptual model for inexact reasoning in rule-based systems. Int. Journal of Approximate Reasoning 3(3), 239–258 (1989)

    Article  MATH  Google Scholar 

  19. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1(1), 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  20. Zadeh, L.A.: A Note on Z-numbers. Information Sciences 181(14), 2923–2932 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  21. Pawlak, Z.: Rough sets. Int. Journal of Parallel Programming 11(5), 341–356 (1982)

    MathSciNet  MATH  Google Scholar 

  22. Ziarko, W., Shan, N.: Discovering attribute relationships, dependencies and rules by using rough sets. In: Proc. 28th Annual Hawaii Int. Conf. on System Sciences, Hawaii, pp. 293–299 (1995)

    Google Scholar 

  23. Grzymala-Busse, J., Wang, A.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values. In: Proc. 5th Int. Workshop on Rough Sets and Soft Computing, pp. 69–72 (1997)

    Google Scholar 

  24. Ilczuk, G., Wakulicz-Deja, A.: Rough Sets Approach to Medical Diagnosis System. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 204–210. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann, Santiago de Chile (1994)

    Google Scholar 

  26. Zaki, M.J.: Scalable algorithms for association mining. IEEE Transactions on Knowledge Data Engineering 12(3), 372–390 (2000)

    Article  MathSciNet  Google Scholar 

  27. Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation. Data Mining Knowledge Discovery 8, 53–87 (2004)

    Article  MathSciNet  Google Scholar 

  28. Berzal, F., Blanco, I., Sánchez, D., Vila, M.-A.: A new framework to assess association rules. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 95–104. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  29. Baqui, S., Just, J., Baqui, S.C.: Deriving strong association rules using a dependency criterion, the lift measure. International Journal of Data Analysis Techniques and Strategies 1(3), 297–312 (2009)

    Article  Google Scholar 

  30. Gaifman, H.: A Theory of Higher Order Probabilities. In: Proc. 1st Conf. on Theoretical Aspects of Reasoning about Knowledge, pp. 275–292. Morgan Kaufmann, Monterey (1986)

    Google Scholar 

  31. Nalepa, G.J., Ligeza, A.: On ALSV Rules Formulation and Inference. In: Lane, H.C., Guesgen, H.W. (eds.) Proc. 2nd Int. FLAIRS Conference, pp. 396–401. AAAI Press, Florida (2009)

    Google Scholar 

  32. Jankowska, B.: Using Semantic Data Integration to Create Reliable Rule-based Systems with Uncertainty. Engineering Applications of Artificial Intelligence 24(8), 1499–1509 (2011)

    Article  Google Scholar 

  33. Jankowska, B.: Evidence-based model for 2-uncertain rules and inexact reasoning. Medical Informatics & Technologies 20, 39–47 (2012)

    Google Scholar 

  34. Jankowska, B., Szymkowiak, M.: On Ranking Production Rules for Rule-Based Systems with Uncertainty. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 546–556. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  35. Szymkowiak, M., Jankowska, B.: Discovering Medical Knowledge from Data in Patients’ Files. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS, vol. 5796, pp. 128–139. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  36. Jankowska, B., Szymkowiak, M.: Designing Medical Production Rules from Semantically Integrated Data. Journal of Medical Informatics & Technologies 16, 95–102 (2010)

    Google Scholar 

  37. Krysicki, W., et al.: Mathematical Statistics. PWN, Warszawa (2006) (in Polish)

    Google Scholar 

  38. Plotnick, L.H., Ducharme, F.M.: Combined Inhaled Anticholinergics and Beta2-Agonists for Initial Treatment of Acute Asthma in Children. The Cochrane Library (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznan University of Technology, Pl. M.Sklodowskiej-Curie 5, 60-965, Poznan, Poland

    Beata Jankowska

  2. Institute of Mathematics, Poznan University of Technology, Pl. M.Sklodowskiej-Curie 5, 60-965, Poznan, Poland

    Magdalena Szymkowiak

Authors
  1. Beata Jankowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Magdalena Szymkowiak
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Jankowska, B., Szymkowiak, M. (2013). Machine Ranking of 2-Uncertain Rules Acquired from Real Data. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence XI. Lecture Notes in Computer Science, vol 8065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41776-4_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41776-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41775-7

  • Online ISBN: 978-3-642-41776-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation