Skip to main content
SpringerLink
Log in

A Granular Computing Paradigm for Concept Learning

  • Chapter
Emerging Paradigms in Machine Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 13))

Abstract

The problem of concept formation and learning is examined from the viewpoint of granular computing. Correspondences are drawn between granules and concepts, between granulations and classifications, and between relations over granules and relations over concepts. Two learning strategies are investigated. A global attribute-oriented strategy searches for a good partition of a universe of objects and a local attribute-value-oriented strategy searches for a good covering. The proposed granular computing paradigm for concept learning offers twofold benefits. Results from concept formulation and learning enrich granular computing and a granular computing viewpoint sheds new light on concept formulation and learning.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahl, V., Allen, T.F.H.: Hierarchy Theory: A Vision, Vocabulary, and Epistemology. Columbia University Press, New York (1996)

    Google Scholar 

  2. Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Boston (2002)

    Book  Google Scholar 

  3. Brown, H.C.: Structural levels in the scientist’s world. The Journal of Philosophy, Psychology and Scientific Methods 13, 337–345 (1916)

    Article  Google Scholar 

  4. Cendrowska, J.: PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27, 349–370 (1987)

    Article  MATH  Google Scholar 

  5. Craik, F.I.M., Lockhart, R.S.: Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior 11, 671–684 (1972)

    Article  Google Scholar 

  6. Floridi, L.: The method of levels of abstraction. Minds and Machines 18, 303–329 (2008)

    Article  Google Scholar 

  7. Foster, C.L., Foster, C.: Algorithms, Abstraction and Implementation: Levels of Detail in Cognitive Science. Academic Press, London (1992)

    Google Scholar 

  8. Goldstone, R.L., Kersten, A.: Concepts and categorization. In: Alice, F.H., Robert, W.P., Irving, B.W. (eds.) Handbook of Psychology, Experimental Psychology, pp. 597–621. Wiley, New Jersey (2003)

    Google Scholar 

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

    Google Scholar 

  10. Grzymala-Busse, J., Rzasa, W.: Approximation space and LEM2-like algorithms for computing local coverings. Fundamenta Informaticae 85, 205–217 (2008)

    MathSciNet  MATH  Google Scholar 

  11. Heil, J.: Levels of reality. Ratio 16, 205–221 (2003)

    Article  Google Scholar 

  12. Hobbs, J.R.: Granularity. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 432–435 (1985)

    Google Scholar 

  13. Hunt, E.B.: Concept Learning: An Information Processing Problem. Wiley, New York (1962)

    Book  Google Scholar 

  14. Margolis, E., Laurence, S.: The ontology of concepts - abstract objects or mental representations? Noûs 41, 561–593 (2007)

    Article  Google Scholar 

  15. Medin, D.L., Smith, E.E.: Concepts and concept formation. Annual Review of Psychology 35, 113–138 (1984)

    Article  Google Scholar 

  16. Ogden, C.K., Richards, I.A.: The Meaning of Meaning: A Study of the Influence of Language upon Thought and of the Science of Symbolism, 8th edn. Harcourt Brace, New York (1946)

    Google Scholar 

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

    MATH  Google Scholar 

  18. Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)

    Google Scholar 

  19. Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  20. Regoczei, S., Hirst, G.: The meaning triangle as a tool for the acquisition of abstract, conceptual knowledge. International Journal of Man-Machine Studies 33, 505–520 (1990)

    Article  Google Scholar 

  21. Smith, E.E., Medin, D.L.: Categories and Concepts. Harvard University Press, Cambridge (1981)

    Google Scholar 

  22. Tzafestas, S.G.: Knowledge Based Systems: Advanced Concepts, Techniques & Applications. World Scientific, New Jersey (1997)

    Book  Google Scholar 

  23. Wuketits, F.M.: Synthetic and analytical thinking. Fresenius’ Journal of Analytical Chemistry 326, 320–323 (1987)

    Article  Google Scholar 

  24. Yao, Y.Y.: Information granulation and rough set approximation. International Journal of Intelligent Systems 16, 87–104 (2001)

    Article  MATH  Google Scholar 

  25. Yao, Y.Y.: Perspectives of granular computing. In: Proceedings of the 2005 IEEE International Conference on Granular Computing, vol. 1, pp. 85–90 (2005)

    Google Scholar 

  26. Yao, Y.Y.: Three perspectives of granular computing. Journal of Nanchang Institute of Technology 25, 16–21 (2006)

    Google Scholar 

  27. Yao, Y.Y.: Granular computing for data mining. In: Proceedings of SPIE Conference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security (2006), doi:10.1117/12.669023

    Google Scholar 

  28. Yao, Y.Y.: The Art of Granular Computing. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 101–112. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  29. Yao, Y.Y.: A unified framework of granular computing. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 401–410. Wiley, New York (2008)

    Chapter  Google Scholar 

  30. Yao, Y.Y.: Granular computing: past, present, and future. In: Proceedings of the 2008 IEEE International Conference on Granular Computing, pp. 80–85 (2008)

    Google Scholar 

  31. Yao, Y.Y.: Integrative levels of granularity. In: Bargiela, A., Pedrycz, W. (eds.) Human-Centric Information Processing Through Granular Modelling, pp. 31–47. Springer, Berlin (2009)

    Chapter  Google Scholar 

  32. Yao, Y.Y.: Interpreting concept learning in cognitive informatics and granular computing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39, 855–866 (2009)

    Article  Google Scholar 

  33. Yao, Y.Y.: Human-inspired granular computing. In: Yao, J.T. (ed.) Novel Developments in Granular Computing: Applications for Advanced Human Reasoning and Soft Computation, pp. 1–15. Information Science Reference, Herskey (2010)

    Chapter  Google Scholar 

  34. Yao, Y.Y., Zhou, B.: A logic language of granular computing. In: Proceedings of the 6th IEEE International Conference on Cognitive Informatics, pp. 178–185 (2007)

    Google Scholar 

  35. Yao, Y.Y., Zhao, Y., Wang, J.: On reduct construction algorithms. In: Proceedings of the 1st International Conference on Rough Sets and Knowledge Technology. LNCS(LNAI), vol. 6042, pp. 297–304 (2006)

    Google Scholar 

  36. Zhao, Y., Yao, Y.Y., Yao, J.T.: Level-wise construction of decision trees for classification. International Journal of Software Engineering and Knowledge Engineering 16, 103–126 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    Yiyu Yao & Xiaofei Deng

Authors
  1. Yiyu Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofei Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiyu Yao .

Editor information

Editors and Affiliations

  1. Deptartment of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipegg, R3B 2E9, Canada

    Sheela Ramanna

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

  3. KES International, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Yao, Y., Deng, X. (2013). A Granular Computing Paradigm for Concept Learning. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28699-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28698-8

  • Online ISBN: 978-3-642-28699-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation