Skip to main content
SpringerLink
Log in

Boosting Based Fuzzy-Rough Pattern Classifier

  • Chapter
  • First Online:
Computational Intelligence in Multi-Feature Visual Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 556))

  • 866 Accesses

Abstract

The boosting-based fuzzy-rough multi-cluster (BFRMC) classification algorithm utilizing the rough set concepts of fuzzy lower and upper approximations is discussed in this chapter. The BFRMC algorithm transforms each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and calculates the fuzzy lower and upper approximations. The membership functions are generated from cluster points generated by the subtractive clustering technique. A certain rule set based on fuzzy lower approximation and a possible rule set based on fuzzy upper approximation are generated. Iterative rule learning based genetic algorithm is employed in combination with a boosting technique, for generating the possible rule set, incrementally optimizing each fuzzy classifier rule. The BFRMC classifier’s performance on the datasets from the UCI machine learning repository is discussed.

Take up one idea. Make that one idea your life—think of it, dream of it, live on that idea. Let the brain, muscles, nerves, every part of your body, be full of that idea, and just leave every other idea alone. This is the way to success

Swami Vivekananda.

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 EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
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

References

  1. L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  2. S. Chiu, Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Sys. 2(3), 18–28 (1994)

    Google Scholar 

  3. Z. Pawlak, Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  4. Z. Pawlak, Rough classification. Int. J. Man Mach. Stud. 20, 469–483 (1984)

    Article  MATH  Google Scholar 

  5. D. Dubois, H. Prade, in Putting rough sets and fuzzy sets together, ed. by R. Slowinski, Intelligent Decision, Support: Handbook of Applications and Advances in Rough Sets Theory, Series D: System Theory, Knowledge Engineering and Problem Solving, vol. 11, (Kluwer Academic Publishers, Dordrecht, 1992), pp. 203–232

    Google Scholar 

  6. D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 191–209 (1990)

    Article  MATH  Google Scholar 

  7. P.K. Pisharady, P. Vadakkepat, A.P. Loh, Hand posture and face recognition using a fuzzy-rough approach. Int. J. Humanoid Rob. 07(03), 331–356 (2010)

    Article  Google Scholar 

  8. P.K. Pisharady, P. Vadakkepat, A.P. Loh, Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets. Appl. Soft. Comput. 11(04), 3429–3440 (2011)

    Article  Google Scholar 

  9. P.K. Pisharady, Computational intelligence techniques in visual pattern recognition, PhD Thesis, National University of Singapore 2011.

    Google Scholar 

  10. P.K Pisharady, P. Vadakkepat, S. Ganesan, A.P. Loh, Boosting based fuzzy-rough pattern classifier, in Proceedings of Trends in Intelligent Robotics: 15th Robot World Cup and Congress, FIRA 2010, Bangalore, India, vol. 103, pp. 306–313, 15–19 Sept 2010.

    Google Scholar 

  11. T.P. Hong, T.T. Wang, S.L. Wang, Knowledge acquisition from quantitative data using the rough-set theory. Intell. Data Anal. 4, 289–304 (2000)

    MATH  Google Scholar 

  12. T.P. Hong, T.T. Wang, S.L. Wang, B.C. Chien, Learning a coverage set of maximally general fuzzy rules by rough sets. Expert Syst. Appl. 19, 97–103 (2000)

    Article  Google Scholar 

  13. Qiang Shen, Alexios Chouchoulas, A rough-fuzzy approach for generating classification rules. Pattern Recogn. 35, 2425–2438 (2002)

    Article  MATH  Google Scholar 

  14. A. Roy, K.P. Sankar, Fuzzy discretization of feature space for a rough set classifier. Pattern Recogn. Lett. 24, 895–902 (2003)

    Article  MATH  Google Scholar 

  15. A. Gonzalez, F. Herrera, Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware Soft. Comput. 4, 233–249 (1997)

    MATH  Google Scholar 

  16. A. Gonzalez, R. Perez, Slave: a genetic learning system based on an iterative approach. IEEE Trans. Fuzzy Syst. 7(2), 176–191 (1999)

    Article  Google Scholar 

  17. C.L. Blake, C.J. Merz, Uci repository of machine learning databases (1998), http://www.ics.uci.edu/ mlearn/MLRepository.html

  18. J.R. Quinlan, Neuro-fuzzy Soft computing (Morgan Kaufmann, San Mateo, 1993)

    Google Scholar 

  19. Y. Shi, R. Eberhart, Y. Chen, Implementation of evolutionary fuzzy systems. IEEE Trans. Fuzzy Syst. 7(2), 109–119 (1999)

    Article  Google Scholar 

  20. H. Ishibuchi, T. Nakashima, T. Murata, Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. Part B. Cybern. 29(5), 601–618 (1999).

    Google Scholar 

  21. A.L. Corcoran, S. Sen, Using real-valued genetic algorithms to evolve rule sets for classification, in Proceedings of first IEEE International Conference on Evolutionary Computing (Orlando, FL), pp. 120–1241, June, 1994.

    Google Scholar 

  22. R.C. Holte, Very simple classification rules perform well on most commonly used dataset. Mach. Learn. 11(1), 63–90 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  23. J.R. Quinlan, Simplifying decision trees. Int. J. Man Mach. Stud. 27, 221–234 (1987)

    Article  Google Scholar 

Download references

Acknowledgments

Figures and tables in this chapter are adapted from ‘Boosting based Fuzzy-Rough Pattern Classifier’, Prahlad Vadakkepat, Pramod Kumar Pisharady, Sivakumar Ganesan, and Loh Ai Poh, Trends in Intelligent Robotics, Springer, Book Series: Communications in Computer and Information Science, Vol.103, Page Nos. 306-313, Copyright @ 2010, with permission from Springer Science+Business Media.

Author information

Authors and Affiliations

  1. Inst. of High Performance Computing, A*STAR, Singapore, Singapore

    Pramod Kumar Pisharady

  2. Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Prahlad Vadakkepat & Loh Ai Poh

Authors
  1. Pramod Kumar Pisharady
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prahlad Vadakkepat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Loh Ai Poh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pramod Kumar Pisharady .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Pisharady, P.K., Vadakkepat, P., Poh, L.A. (2014). Boosting Based Fuzzy-Rough Pattern Classifier. In: Computational Intelligence in Multi-Feature Visual Pattern Recognition. Studies in Computational Intelligence, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-287-056-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-287-056-8_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-055-1

  • Online ISBN: 978-981-287-056-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation