Skip to main content

Pattern Classification Based on Conventional Interpretation of MFI

  • Chapter
  • First Online:
Soft Computing Approach to Pattern Classification and Object Recognition
  • 1034 Accesses

Abstract

Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by Pedrycz (Pattern Recognition 23 (1/2), 121–146, 1990). In the course of doing so, we first consider a particular interpretation of the multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. Subsequently, we introduce the notion of a fuzzy pattern vector to represent a population of training patterns in the pattern space and to denote the antecedent part of the said particular interpretation of the MFI. We introduce a new approach to the computation of the derivative of the fuzzy max-function and min-function using the concept of a generalized function. During the construction of the classifier based on FRC, we use fuzzy linguistic statements (or fuzzy membership function to represent the linguistic statement) to represent the values of features (e.g., feature F 1 is small and F 2 is big) for a population of patterns. Note that the construction of the classifier essentially depends on the estimate of a fuzzy relation ℜ between the input (fuzzy set) and output (fuzzy set) of the classifier. Once the classifier is constructed, the nonfuzzy features of a pattern can be classified. At the time of classification of the nonfuzzy features of the test patterns, we use the concept of fuzzy masking to fuzzify the nonfuzzy feature values of the test patterns. The performance of the proposed scheme is tested on synthetic data. Finally, we use the proposed scheme for the vowel classification problem of an Indian language.

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

Access this chapter

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
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

References

  • J. C. Bezdek, S. K. Pal (eds.), Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data (IEEE, New York, 1992)

    Google Scholar 

  • G. Bortolan, R. Degani, Ranking of fuzzy alternatives in electrocardiography, in Fuzzy Information, Knowledge Representation and Decision, Analysis, ed. by E. Sanchez, M. M. Gupta (Pergamon, Oxford, 1983), pp. 397–402

    Google Scholar 

  • G. Bortolan, R. Degani, K. Hirota, W. Pedrycz Classification of ECG Signals-utilization of fuzzy pattern matching, in Proceedings International Workshop Fuzzy System (Applic, Iizuka, 1988)

    Google Scholar 

  • M. K. Chakraborty, Some aspects of [0, 1] fuzzy relations and a few suggestions toward its use. Approx. Reas. Expert Syst. pp. 139–157 (1985)

    Google Scholar 

  • R. Degani, G. Bortolan, Computerized electrocardiogram diagnosis: Fuzzy approach, in Encyclopedia of Systems and Control. (Pergamon, Oxford, 1987)

    Google Scholar 

  • R. Degani, G. Bortolan, Fuzzy numbers in computerized electrocardiography. Fuzzy Sets Syst. 24, 345–362 (1987)

    Article  Google Scholar 

  • A. Dinola, W. Pedrycz, S. Sessa, E. Sanchez, Fuzzy relation equations theory as a basis of fuzzy modeling: An overview. Fuzzy Sets Syst. 40, 415–429 (1991)

    Article  MathSciNet  Google Scholar 

  • D. Dubois, M. C. Jaulent, Techniques for extracting fuzzy regions, in First IFSA Congress, vol. II (Mallorca, Spain, 1985), 1–6 July

    Google Scholar 

  • S. Gottwald, Approximately solving fuzzy relation equations: Some mathematical results and some heuristic proposals. Fuzzy Sets Syst. 66, 175–193 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  • H. Hellendoorn, The generalized modus ponens considered as a fuzzy relation. Fuzzy Sets Syst. 48, 29–48 (1992)

    Article  MathSciNet  Google Scholar 

  • K. Hirota, Fuzzy robot vision and fuzzy controlled robot, in NATO ASI CIM, ed. by I.B. Turksen (Springer, Berlin, 1988)

    Google Scholar 

  • K. Hirota, K. Iwami, W. Pedrycz, FCM-AD (fuzzy cluster means with additional ata) and its application to aerial images, in Proc. II IFSA Congress, vol. II (Tokyo, Japan, 1987), pp. 729–732

    Google Scholar 

  • T. L. Huntsberger, C. L. Jacobs, R. L. Canon, Iterative fuzzy image segmentation. Pattern Recognit. 18, 131–138 (1985)

    Article  Google Scholar 

  • T. L. Huntsberger, C.H. Rangarajan, S.N. Jayaramurthy, Representation of uncertainty in computer vision using fuzzy sets. IEEE Trans. Comput. C–2, 145–156 (1986)

    Article  Google Scholar 

  • N. Ikoma, W. Pedrycz, K. Hirota, Estimation of fuzzy relational matrix by using probabilistic descent method. Fuzzy Sets Syst. 57, 335–349 (1993)

    Article  MathSciNet  Google Scholar 

  • W. J. Kickert, H. Koppleaar, Application of fuzzy set theory to syntactic pattern recognition of handwritten capitals. IEEE Trans. Syst. Man Cybern. SMC–6, 148–151 (1986)

    Google Scholar 

  • A. Kumar, A real-time system for pattern recognition of human sleep stages by fuzzy systems analysis. Pattern Recognit. 9, 43–46 (1977)

    Article  Google Scholar 

  • E. T. Lee, Proximity measure for the classification of geometric figures. J. Cybern. 2, 43–59 (1972)

    Article  Google Scholar 

  • M. Mizumoto, Extended fuzzy reasoning, in Approximate Reasoning in Expert Systems, ed. by M. M. Gupta, A. Kandel, W. Bandler, J. B. Kiszka (North-Holland, Amsterdam, 1985), pp. 71–85

    Google Scholar 

  • R. Di Mori, Computerized Models of Speech Using Fuzzy Algorithms (Plenum, New York, 1983)

    Book  Google Scholar 

  • R. Di Mori, P. Laface, Use of fuzzy algorithms for phonetic and phonetic labeling of continuous speech. IEEE Trans. Pattern Anal. Machine Intell. 2, 136–148 (1980)

    Article  Google Scholar 

  • S. V. Ovchinnikov, T. Riera, On fuzzy classifications. Fuzzy Sets Syst. 49, 119–132 (1992)@@@@

    Article  Google Scholar 

  • Y. H. Pao, Adaptive Pattern Recognition and Neural Networks (Addison Wesley Publishing Company, Boston, 1989)

    MATH  Google Scholar 

  • W. Pedrycz, Numerical and applications aspects of fuzzy relational equation. Fuzzy Sets Syst. 11, 1–18 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  • W. Pedrycz, Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data. Fuzzy Sets Syst. 16, 163–174 (1985a)

    Article  MathSciNet  MATH  Google Scholar 

  • W. Pedrycz, On generalized fuzzy relational equations and their applications. J. Math. Anal. Appl. 107, 520–536 (1985b)

    Article  MathSciNet  MATH  Google Scholar 

  • W. Pedrycz ECG Signal classification with the aid of linguistic classifier, in Proceedings XIV International Conference Medicine Biomedical Engineering, (Spain, 1985c) pp. 11–16 Aug

    Google Scholar 

  • W. Pedrycz, Approximate solution of Fuzzy relational equations. Fuzzy Sets Syst. 28, 183–201 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  • W. Pedrycz, Fuzzy sets in pattern recognition methodology and methods. Pattern Recogn. 23(1/2), 121–146 (1990)

    Article  Google Scholar 

  • W. Pedrycz, Processing of relational structures: Fuzzy relational equations. Fuzzy Sets Syst. 40, 77–106 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  • W. Pedrycz, Genetic algorithms for learning in fuzzy relational structures. Fuzzy Sets Syst. 69(1), 37–52 (1995)

    Article  Google Scholar 

  • L. Saitta, P. Tarasso, Fuzzy characteristics of coronary disease. Fuzzy Sets Syst. 5, 245–258 (1981)

    Article  MATH  Google Scholar 

  • A. Seif, J. Aguilar-Martin, Multi-group classification using fuzzy correlation. Fuzzy Sets Syst. 3, 109–122 (1980)

    Article  MATH  Google Scholar 

  • M. Shimura, Applications of fuzzy set theory to pattern recognition. J. JAACE 19, 243–248 (1975)

    MathSciNet  Google Scholar 

  • P. K. Simpson, Fuzzy min-max neural network-Part 1: Classification. IEEE Trans. Neural Networks 13, 776–786 (1992)

    Article  Google Scholar 

  • P. Siy, C. S. Chen, Fuzzy logic for handwritten numerical character recognition. IEEE Trans. Syst. Man Cybern SMC–4, 570–575 (1974)

    Google Scholar 

  • M. Sugeno, T. Takagi, Multidimensional fuzzy reasoning. Fuzzy Sets Syst. 9, 313–325 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  • Y. Tsukamoto, An approach to fuzzy reasoning method, in Advance in Fuzzy Set Theory and Applications, ed. by M. M. Gupta, R. K. Ragade, R. R. Yager (North-Holland, Amsterdam, 1979), pp. 137–149

    Google Scholar 

  • H. F. Wang, Numerical analysis on fuzzy relation equations with various operators. Fuzzy Sets Syst. 53, 155–166 (1993)

    Article  MATH  Google Scholar 

  • W. U. Wangming, Fuzzy reasoning and fuzzy relational equations. Fuzzy Sets Syst. 20, 67–78 (1986)

    Article  MATH  Google Scholar 

  • S. Watanabe, Pattern Recognition: Human and Mechanical (Wiley, New York, 1985)

    Google Scholar 

  • M. A. Woodbury, J. Clive, Clinical pure types as fuzzy partition. J. Cybern. 3, 111–121 (1974)

    Google Scholar 

  • L. A. Zadeh, Theory of Approximate Reasoning, in Machine Intelligence, ed. by D. Michie, L. I. Mikulich, J. E. Hayes (Ellis Horwood, Chichester, 1970), pp. 149–194

    Google Scholar 

  • L. A. Zadeh, K. S. Fu, K. Tanaka, M. Shimura (eds.), Fuzzy Sets and Their Applications to Cognitive and Decision Processes (Academic, New York, 1975)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kumar S. Ray .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media New York

About this chapter

Cite this chapter

Ray, K.S. (2012). Pattern Classification Based on Conventional Interpretation of MFI. In: Soft Computing Approach to Pattern Classification and Object Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5348-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-5348-2_2

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-5347-5

  • Online ISBN: 978-1-4614-5348-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics