Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Rule Induction from Rough Approximations

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

  • 11k Accesses

Abstract

Rule induction is an important technique in data mining or machine learning. Knowledge is frequently expressed by rules in many areas of artificial intelligence (GlossaryTerm

AI

), including rule-based expert systems. In this chapter we discuss only supervised learning in which all cases of the input data set are pre-classified by an expert.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 269.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 349.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

Abbreviations

AI:

artificial intelligence

LEM:

learning from examples module

LERS:

learning from examples using rough sets

MLEM2:

modified LEM2 algorithm

References

  1. Z. Pawlak: Rough sets, Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  3. J.W. Grzymala-Busse: Rule induction. In: Data Mining and Knowledge Discovery Handbook, Second Edition, ed. by O. Maimon, L. Rokach (Springer, Berlin, Heidelberg 2010) pp. 249–265

    Google Scholar 

  4. Z. Pawlak, J.W. Grzymala-Busse, R. Slowinski, W. Ziarko: Rough sets, Commun. ACM 38, 89–95 (1995)

    Article  Google Scholar 

  5. J.G. Bazan, M.S. Szczuka, A. Wojna, M. Wojnarski: On the evolution of rough set exploration system. In: Rough Sets and Current Trends in Computing, ed. by S. Tsumoto, R. Słowiński, J. Komorowski, J.W. Grzymala-Busse (Springer, Berlin, Heidelberg 2004) pp. 592–601

    Chapter  Google Scholar 

  6. J.W. Grzymala-Busse: LERS – A system for learning from examples based on rough sets. In: Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, ed. by R. Slowinski (Kluwer Academic, Boston 1992) pp. 3–18

    Google Scholar 

  7. J. Stefanowski: Algorithms of Decision Rule Induction in Data Mining (Poznan University of Technology Press, Poznan 2001)

    Google Scholar 

  8. L.B. Booker, D.E. Goldberg, J.F. Holland: Classifier systems and genetic algorithms. In: Machine Learning. Paradigms and Methods, ed. by J.G. Carbonell (MIT Press, Cambridge 1990) pp. 235–282

    Google Scholar 

  9. J.H. Holland, K.J. Holyoak, R.E. Nisbett, P.R. Thagard: Induction. Processes of Inference, Learning, and Discovery (MIT Press, Cambridge 1986)

    Google Scholar 

  10. M.R. Chmielewski, J.W. Grzymala-Busse: Global discretization of continuous attributes as preprocessing for machine learning, Int. J. Approx. Reason. 15(4), 319–331 (1996)

    Article  MATH  Google Scholar 

  11. J.W. Grzymala-Busse: Discretization of numerical attributes. In: Handbook of Data Mining and Knowledge Discovery, ed. by W. Kloesgen, J. Zytkow (Oxford Univ. Press, Oxford 2002) pp. 218–225

    Google Scholar 

  12. J.W. Grzymala-Busse: Mining numerical data – A rough set approach, Trans. Rough Sets 11, 1–13 (2010)

    Google Scholar 

  13. J.W. Grzymala-Busse: MLEM2: A new algorithm for rule induction from imperfect data, Proc. 9th Int. Conf. Inform. Proc. Manag. Uncertain. Knowl.-Based Syst. (2002) pp. 243–250

    Google Scholar 

  14. J.W. Grzymala-Busse: Data with missing attribute values: Generalization of indiscernibility relation and rule induction, Trans. Rough Sets 1, 78–95 (2004)

    MATH  Google Scholar 

  15. J.W. Grzymala-Busse: Rough set strategies to data with missing attribute values, Proc. Workshop Found. New Dir. Data Min. (2003) pp. 56–63

    Google Scholar 

  16. J.W. Grzymala-Busse: Characteristic relations for incomplete data: A generalization of the indiscernibility relation. In: Rough Sets and Current Trends in Computing, ed. by S. Tsumoto, R. Słowiński, J. Komorowski, J.W. Grzymala-Busse (Springer, Berlin, Heidelberg 2004) pp. 244–253

    Chapter  Google Scholar 

  17. T.Y. Lin: Topological and fuzzy rough sets. In: Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, ed. by R. Slowinski (Kluwer Academic, Boston 1992) pp. 287–304

    Google Scholar 

  18. R. Slowinski, D. Vanderpooten: A generalized definition of rough approximations based on similarity, IEEE Trans. Knowl. Data Eng. 12, 331–336 (2000)

    Article  Google Scholar 

  19. J.W. Grzymala-Busse, W. Ziarko: Data mining based on rough sets. In: Data Mining: Opportunities and Challenges, ed. by J. Wang (Idea Group, Hershey 2003) pp. 142–173

    Chapter  Google Scholar 

  20. J.W. Grzymala-Busse, Y. Yao: Probabilistic rule induction with the LERS data mining system, Int. J. Intell. Syst. 26, 518–539 (2011)

    Article  Google Scholar 

  21. Z. Pawlak, A. Skowron: Rough sets: Some extensions, Inf. Sci. 177, 28–40 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  22. Z. Pawlak, S.K.M. Wong, W. Ziarko: Rough sets: probabilistic versus deterministic approach, Int. J. Man-Mach. Stud. 29, 81–95 (1988)

    Article  MATH  Google Scholar 

  23. Y.Y. Yao: Probabilistic rough set approximations, Int. J. Approx. Reason. 49, 255–271 (2008)

    Article  MATH  Google Scholar 

  24. Y.Y. Yao, S.K.M. Wong: A decision theoretic framework for approximate concepts, Int. J. Man-Mach. Stud. 37, 793–809 (1992)

    Article  Google Scholar 

  25. W. Ziarko: Variable precision rough set model, J. Comput. Syst. Sci. 46(1), 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  26. W. Ziarko: Probabilistic approach to rough sets, Int. J. Approx. Reason. 49, 272–284 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  27. P.G. Clark, J.W. Grzymala-Busse: Experiments on probabilistic approximations, IEEE Int. Conf. Granul. Comput. (2011) pp. 144–149

    Google Scholar 

  28. P.G. Clark, J.W. Grzymala-Busse, M. Kuehnhausen: Local probabilistic approximations for incomplete data, Lect. Notes Comput. Sci. 7661, 93–98 (2012)

    Article  MATH  Google Scholar 

  29. J.W. Grzymala-Busse, W. Rzasa: A local version of the MLEM2 algorithm for rule induction, Fundam. Inform. 100, 99–116 (2010)

    MathSciNet  MATH  Google Scholar 

  30. C. Cohagan, J.W. Grzymala-Busse, Z.S. Hippe: Experiments on mining inconsistent data with bagging and the MLEM2 rule induction algorithm, Int. J. Granul. Comput. Rough Sets Intell. Syst. 2, 257–271 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dep. Electrical Engineering and Computer Science, University of Kansas, 3014 Eaton Hall, 1520 W. 15th St., KS 66045-7621, Lawrence, USA

    Jerzy W. Grzymala-Busse

Authors
  1. Jerzy W. Grzymala-Busse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Grzymala-Busse, J.W. (2015). Rule Induction from Rough Approximations. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-43505-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation