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An evaluation of retranslation methods in computing with words

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Abstract

To represent output fuzzy values of a computing with words (CW) system in natural language, a retranslation unit is required. In this work, retranslation methods applicable to a CW system are explored. Several methods that employ similarity measures of fuzzy sets, linguistic modifiers, or linguistic quantifiers have been applied to three real-world case studies. Performances of the applied methods have been evaluated through degree of validity, and comparison of characteristics of fuzzy sets such as fuzziness and specificity. Results show that invalid linguistic terms might be used in the retranslation process which also cause incomprehensible phrases in natural language.

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Notes

  1. Eq. (11) should not be confused with definition of fuzziness in Bonissone method in Eq. (7).

References

  • Bonissone P (1978) B. D. o. E. E. University of California, and C. Sciences. A pattern recognition approach to the problem of linguistic approximation in system analysis

  • Bonissone PP (1980) A fuzzy sets based linguistic approach: theory and applications. In: Proceedings of the 12th conference on Winter simulation, ser. WSC ’80. Piscataway, NJ, USA: IEEE Press, pp 99–111. [Online]. Available: http://dl.acm.org/citation.cfm?id=800286.809395

  • Buckley J, Eslami E (2002) An Introduction to Fuzzy Logic and Fuzzy Sets, ser. Advances in Intelligent and Soft Computing. Physica-Verlag HD, [Online]. Available: http://books.google.com/books?id=IzN93YlrKlwC

  • Bush R, Mosteller F (1951) A model for stimulus generalization and discrimination. Psychol Rev 58(6):413

    Article  Google Scholar 

  • Bustince H, Pagola M, Barrenechea E (2007) Construction of fuzzy indices from fuzzy di-subsethood measures: application to the global comparison of images. Inf Sci 177(3):906–929

    Article  MATH  MathSciNet  Google Scholar 

  • Cross V, Sudkamp T (2002) Similarity and compatibility in fuzzy set theory: assessment and applications, vol 93. Springer, Heidelberg

    Google Scholar 

  • Degani R, Bortolan G (1988) The problem of linguistic approximation in clinical decision making. Int J Approx Reason 2(2):98

    Article  Google Scholar 

  • Dubois D, Prade H (1985) Fuzzy cardinality and the modeling of imprecise quantification. Fuzzy sets Syst 16(3):199–230

    Article  MATH  Google Scholar 

  • DuBois D, Prade H (1980) Fuzzy sets and systems: theory and applications, Academic Press, vol 144

  • Dvořák A (1999) On linguistic approximation in the frame of fuzzy logic deduction. Soft Comput 3(2):111–115

    Article  Google Scholar 

  • Eshragh F, Mamdani E (1979) A general approach to linguistic approximation. Int J Man Mach Stud 11(4):501–519

    Article  MATH  MathSciNet  Google Scholar 

  • Huang H, Kuo Y (2010) Cross-lingual document representation and semantic similarity measure: a fuzzy set and rough set based approach. IEEE Trans Fuzzy Syst 18(6):1098–1111

    Article  Google Scholar 

  • Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bulletin de la Sociète Vaudense des Sciences Naturelles 44:223–270

  • Kailath T (1967) The divergence and bhattacharyya distance measures in signal selection. IEEE Trans Commun Technol 15(1):52–60

    Article  Google Scholar 

  • Khorasani ES, Rahimi S, Patel P, Houle D (2011) Cwjess: an expert system shell for computing with words. In: IRI, pp 396–399

  • Khorasani E, Patel P, Rahimi S, Houle D (2012) An inference engine toolkit for computing with words. EnglishJournal of Ambient Intelligence and Humanized Computing, pp 1–20, [Online]. Available: http://dx.doi.org/10.1007/s12652-012-0137-8

  • Kowalczyk R (1999) On quantified linguistic approximation. In: Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, ser. UAI’99. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., pp 351–358. [Online]. Available: http://dl.acm.org/citation.cfm?id=2073796.2073836

  • Marhamati N, Patel P, Khorasani ES, Rahimi S (2013) Towards retranslation of fuzzy values in computing with words. In: Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, Edmonton, pp 922–929

  • Mendel JM (2001) The perceptual computer: an architecture for computing with words. In: FUZZ-IEEE, pp 35–38

  • Mendel J, Wu D (2010) Perceptual computing: aiding people in making subjective judgments, ser IEEE press series on computational intelligence. Wiley, New York

  • Reformat M, Ly C (2009) Ontological approach to development of computing with words based systems. Int J Approx Reason 50(1): 72–91

  • Restle F (1959) A metric and an ordering on sets. Psychometrika 24(3):207–220

  • Tversky A (1977) Features of similarity. Psychol Rev 84(4):327

    Article  Google Scholar 

  • Wenstøp F (1980) uantitative analysis with linguistic values. Fuzzy Sets Syst 4(2):99–115

    Article  MATH  Google Scholar 

  • Whalen T, Schott B (2001) Empirical comparison of techniques for linguistic approximation. In: IFSA World Congress and 20th NAFIPS International Conference (2001) Joint 9th, vol 1, IEEE, pp 93–97

  • Wu D, Mendel J (2008) A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets. Inf Sci 178(2):381–402

    Article  MATH  MathSciNet  Google Scholar 

  • Yager RR (2004) On the retranslation process in zadeh’s paradigm of computing with words. IEEE Trans Syst Man Cybern B 34(2):1184–1195

  • Zadeh LA (2000) From computing with numbers to computing with words from manipulation of measurements to manipulation of perceptions. In: Azvine B, Azarmi N, Nauck DD (eds) Intelligent systems and soft computing: prospects, tools and applications, vol 1804. Springer, London, pp 3–40

  • Zadeh LA (2009) Computing with words and perceptions - a paradigm shift. In: Proceedings of the IEEE international conference on information reuse and integration, IRI 2009, 10–12 August 2009. IEEE Systems, Man, and Cybernetics Society, Las Vegas, Nevada, USA

  • Zadeh L (1996) Fuzzy logic= computing with words. IEEE Trans Fuzzy Syst 4(2):103–111

    Article  MathSciNet  Google Scholar 

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Correspondence to Shahram Rahimi.

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Communicated by V. Loia.

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Marhamati, N., Rahimi, S., Patel, P. et al. An evaluation of retranslation methods in computing with words. Soft Comput 18, 2061–2073 (2014). https://doi.org/10.1007/s00500-014-1333-6

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