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Web shopping expert using new interval type-2 fuzzy reasoning

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Abstract

Finding a product with high quality and reasonable price online is a difficult task due to uncertainty of Web data and queries. In order to handle the uncertainty problem, the Web Shopping Expert, a new type-2 fuzzy online decision support system, is proposed. In the Web Shopping Expert, a fast interval type-2 fuzzy method is used to directly use all rules with type-1 fuzzy sets to perform type-2 fuzzy reasoning efficiently. The parameters of type-2 fuzzy sets are optimized by a least square method. The Web Shopping Expert based on the interval type-2 fuzzy inference system provides reasonable decisions for online users.

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References

  • Zadeh LA (1975) The Concept of a linguistic variable and its application to approximate reasoning – I. Inf Sci 8:199–249

    Article  MathSciNet  Google Scholar 

  • Mowrer HT (2000) Uncertainty in natural resource decision support systems: sources, interpretation, and importance. Comput Electron Agric 27:139–154

    Article  Google Scholar 

  • Andriole SJ (1989) Handbook of decision support systems. tab publishers, Blue Ridge Summit, Penn

    Google Scholar 

  • Phad TD (2002) Fuzzy queries, search, and decision support system. Soft Comput. 6:373–399

    Google Scholar 

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

    Article  Google Scholar 

  • Choo EU, Schoner B, Wedley WC (1999) Interpretation of criteria weights in multi-criteria decision making. Comput Ind Eng 37: 527–541

    Article  Google Scholar 

  • Nozaki K, Ishibuchi H, Tanaka H (1997) A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets Syst 86:251–270

    Article  Google Scholar 

  • Figueiredo M, Gomide F (1999) Design of fuzzy systems using neurofuzzy networks. IEEE Trans Neural Netw 10:815–827

    Article  Google Scholar 

  • Herrera F, Verdegay JL (1996) Genetic algorithms and soft computing. Physica-Verlag, Berlin

    MATH  Google Scholar 

  • Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22:1414–1427

    Article  MathSciNet  Google Scholar 

  • Grauel A, Renners I Ludwig LA (2000) Optimizing fuzzy classifiers by evolutionary algorithms. In: Proceedings of the IEEE 4th international conference on knowledge-based intelligent engineering systems and allied technologies

  • Klawonn F (1994) Fuzzy sets and vague environments. fuzzy sets Syst 66:207–221

    Article  MATH  MathSciNet  Google Scholar 

  • Hong T-P, Lee C-Y (1998) Learning fuzzy knowledge from training examples. In: Proceedings of the seventh international conference on information and knowledge management pp 161–166

  • Zhang Y-Q (2003) Fuzzy logic. The Internet encyclopedia. In: Bidgoli, H et al. (eds) Wiley Hoboken

  • Jang J-S, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice Hall

  • Wang LX, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans Neural Netw 5:807–814

    Article  Google Scholar 

  • Setnes M, Hellendoom H (2000) Orthogonal transforms for ordering and reduction of fuzzy rules. In: Proceedings of the 9th IEEE international conference on fuzzy systems pp 700–705

  • Yager RR (1980) Fuzzy subsets of type II in decision. J Cybern 10:137–159

    MathSciNet  Google Scholar 

  • Kamik NN, Mendel JM, Liang QL (1999) Type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 7:643–658

    Article  Google Scholar 

  • Lawson CL, Hanson RJ (1995) Solving least squares problems. SIAM, Philadelphia

    MATH  Google Scholar 

  • Mizumoto M, Tanaka K (1976) Some properties of fuzzy sets of type-2. Inf Contr 31:312–340

    Article  MathSciNet  Google Scholar 

  • Kamik NN, Mendel JM (1998) Introduction to type-2 fuzzy logic systems of Proceedings. In:IEEE fuzzy Conference, pp 5–920

  • Liang QL, Mendel JM (2000) Interval type-2 fuzzy logic system: theory and design. IEEE Trans Fuzzy Syst 8: 535–550

    Article  Google Scholar 

  • Taylor JR (1997) Introduction to error analysis. University Science Books

  • Bevington PR (1969) Data reduction and error analysis for the physical sciences. McGraw-Hill, New York

    Google Scholar 

  • Casillas J, Cordon O, Herrera F, Magdalena L (2003) Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview. Interpretability issues in fuzzy modeling. Springer, Berlin Heidelberg New York

  • Zadeh LA, Nikravesh M (2002) Perception-based intelligent decision systems, AINS; ONR Summer 2002 Program Review, UCLA

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Correspondence to L. Gu.

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Gu, L., Zhang, Y.Q. Web shopping expert using new interval type-2 fuzzy reasoning. Soft Comput 11, 741–751 (2007). https://doi.org/10.1007/s00500-006-0117-z

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  • DOI: https://doi.org/10.1007/s00500-006-0117-z

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