Building a type-2 fuzzy regression model based on creditability theory

Yicheng Wei, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Information in real life may have linguistically vagueness. Thus, type-1 fuzzy set was introduced to model this uncertainty. Additionally, same words will mean variously to different people, which means uncertainty also exists when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set is then invented to express the hybrid uncertainty of both primary fuzziness and secondary one of membership functions. On the one hand, type-2 fuzzy variable models the vagueness of information better. On the other hand, those variables are hard to deal with its three-dimensional feature given. To address problems in presence of such variables with hybrid fuzziness, a new class of type-2 fuzzy regression model is built based on credibility theory, and is called the T2 fuzzy expected value regression model. The new model will be developed into two forms: form-A and form-B. This paper is a further work based on our former research of type-2 fuzzy qualitative regression model.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    DOIs
    Publication statusPublished - 2013
    Event2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad
    Duration: 2013 Jul 72013 Jul 10

    Other

    Other2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
    CityHyderabad
    Period13/7/713/7/10

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    Keywords

    • Creditability theory
    • Expected value
    • Regression model
    • Type-2 fuzzy set

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science

    Cite this

    Wei, Y., & Watada, J. (2013). Building a type-2 fuzzy regression model based on creditability theory. In IEEE International Conference on Fuzzy Systems [6622562] https://doi.org/10.1109/FUZZ-IEEE.2013.6622562