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Effect of linguistic hedges on General Type-2 fuzzy representation of linear adjectives

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Abstract

This paper addresses the ‘modeling’ aspect of the computing with words (CWW) paradigm. The objective is to offer the computations for fuzzy modeling of phrases consisting of linear adjectives and linguistic hedges. The study conducted is novel in view that the effect of linguistic hedges on the Type-2 representation of the linear adjectives is investigated, particularly the Linear General Type-2 (LGT2) representation reported lately in the literature. Thus, the paper contributes to outline the General Type-2 representation of the phrases such as very tall, more or less short, etc. Particularly, the study finds application in the assignment of membership functions to the linguistic labels in complex fuzzy logic system which serves to complex CWW problems. The implementation carried out for the conducted study reports results that are in agreement with the effect caused by the linguistic hedges.

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Data availability

The data specification for implementation is mentioned under Section 3 of the manuscript. The ouptut data is tabulated in Tables 1 and 2 of the same.

Code availability

The code implemented in Java language for the work carried out and the related data is available.

References

  1. Aguiar H (2019) A topological perspective for interval type-2 fuzzy hedges (preprint)

  2. Bilgin A, Hagras H, Malibari A, Alhaddad MJ, Alghazzawi D (2012) Towards a general type-2 fuzzy logic approach for computing with words using linear adjectives. In: 2012 IEEE international conference on fuzzy systems, pp 1–8. IEEE

  3. Bilgin A, Hagras H, Malibari A, Alhaddad MJ, Alghazzawi D (2013) Towards a linear general type-2 fuzzy logic based approach for computing with words. Soft Comput 17(12):2203–2222

    Article  Google Scholar 

  4. Civanlar MR, Trussell HJ (1986) Constructing membership functions using statistical data. Fuzzy Sets Syst 18(1):1–13

    Article  MathSciNet  Google Scholar 

  5. Herrera F, Herrera-Viedma E, Martınez L (2000) A fusion approach for managing multi-granularity linguistic term sets in decision making. Fuzzy Sets Syst 114(1):43–58

    Article  Google Scholar 

  6. Herrera-Viedma E, López-Herrera AG (2007) A model of an information retrieval system with unbalanced fuzzy linguistic information. Int J Intell Syst 22(11):1197–1214

    Article  Google Scholar 

  7. Herrera-Viedma E, López-Herrera AG, Luque M, Porcel C (2007) A fuzzy linguistic IRS model based on a 2-tuple fuzzy linguistic approach. Int J Uncertain Fuzziness Knowl Based Syst 15(02):225–250

    Article  Google Scholar 

  8. Herrera-Viedma E, Peis E, Morales-del Castillo JM, Alonso S, Anaya K (2007) A fuzzy linguistic model to evaluate the quality of web sites that store XML documents. Int J Approx Reason 46(1):226–253

    Article  Google Scholar 

  9. Jamsandekar SS, Mudholkar RR (2014) Fuzzy classification system by self generated membership function using clustering technique. BVICA M’s Int J Inf Technol 6(1):697

    Google Scholar 

  10. Kacprzyk J, Zadrozny S (2005) Linguistic database summaries and their protoforms: toward natural language based knowledge discovery tools. Inf Sci 173:281–304

    Article  MathSciNet  Google Scholar 

  11. Martinez L, Barranco MJ, Pérez LG, Espinilla M (2008) A knowledge based recommender system with multigranular linguistic information. Int J Comput Intell Syst 1(3):225–236

    Article  Google Scholar 

  12. Medasani S, Kim J, Krishnapuram R (1998) An overview of membership function generation techniques for pattern recognition. Int J Approx Reason 19(3–4):391–417

    Article  MathSciNet  Google Scholar 

  13. Mendel JM (2003) Fuzzy sets for words: a new beginning. In: The 12th ieee international conference on fuzzy systems, 2003. FUZZ’03, vol 1, pp 37–42. IEEE

  14. Mendel JM (2014) General type-2 fuzzy logic systems made simple: a tutorial. IEEE Trans Fuzzy Syst 22(5):1162–1182

    Article  Google Scholar 

  15. Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul Comput 1(1):59–69

    Article  Google Scholar 

  16. Mendel JM (2017) Uncertain rule-based fuzzy systems. In: Introduction and new directions. Springer, p 684

  17. Mendel JM (2019) Type-2 fuzzy sets as well as computing with words. IEEE Comput Intell Mag 14(1):82–95

    Article  Google Scholar 

  18. Mendel JM, John RB (2002) Type-2 fuzzy sets made simple. IEEE Trans Fuzzy Syst 10(2):117–127

    Article  Google Scholar 

  19. Niewiadomski A (2008) A type-2 fuzzy approach to linguistic summarization of data. IEEE Trans Fuzzy Syst 16(1):198–212

    Article  MathSciNet  Google Scholar 

  20. Siddique B, Beg MS (2019) “Computing with words” -based semantic similarity measure for adjective phrases. In: 2019 International conference on electrical, electronics and computer engineering (UPCON), pp 1–5. IEEE

  21. Turksen I, Resconi G (2006) Fuzzy truthoods based on an additive semantic measure with break of global symmetry in modal logic. Int J Fuzzy Syst 8(1):14–38

    MathSciNet  Google Scholar 

  22. Verkuilen J (2005) Assigning membership in a fuzzy set analysis. Sociol Methods Res 33(4):462–496

    Article  MathSciNet  Google Scholar 

  23. Wang TC, Chang TH (2007) Forecasting the probability of successful knowledge management by consistent fuzzy preference relations. Expert Syst Appl 32(3):801–813

    Article  Google Scholar 

  24. Wu D, Mendel JM (2007) Aggregation using the linguistic weighted average and interval type-2 fuzzy sets. IEEE Trans Fuzzy Syst 15(6):1145–1161

    Article  Google Scholar 

  25. Zadeh LA (1972) Fuzzy-set-theoretic interpretation of linguistic hedges. J Cybern 2(3):4–34

    Article  MathSciNet  Google Scholar 

  26. Zadeh LA (1999) From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions. IEEE Trans Circuits Syst I Fundam Theory Appl 46(1):105–119

    Article  MathSciNet  Google Scholar 

  27. Zadeh LA (1999) Fuzzy logic= computing with words. In: Computing with words in information/intelligent systems 1, pp 3–23. Springer

  28. Zadrozny S, Kacprzyk J (2006) Computing with words for text processing: an approach to the text categorization. Inf Sci 176(4):415–437

    Article  MathSciNet  Google Scholar 

  29. Zhu AX, Yang L, Li B, Qin C, Pei T, Liu B (2010) Construction of membership functions for predictive soil mapping under fuzzy logic. Geoderma 155(3–4):164–174

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Aligarh Muslim University, Aligarh, India

    Bushra Siddique & M. M. Sufyan Beg

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  1. Bushra Siddique
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  2. M. M. Sufyan Beg
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Correspondence to Bushra Siddique.

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Siddique, B., Beg, M.M.S. Effect of linguistic hedges on General Type-2 fuzzy representation of linear adjectives. Int. j. inf. tecnol. 13, 1217–1220 (2021). https://doi.org/10.1007/s41870-021-00635-9

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  • DOI: https://doi.org/10.1007/s41870-021-00635-9

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