Building linguistic random regression model from the perspective of type-2 fuzzy set

Fei Song, Shinya Imai, Junzo Watada

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

    Abstract

    Information given in linguistic terms around real life sometimes is vague in meaning, as type-1 fuzzy set was introduced to modulate this uncertainty. Meanwhile, same word may result in various meaning to people, indicating the uncertainty also exist when associated with the membership function of a type-1 fuzzy set. Type-2 fuzzy set attempt to express the hybrid uncertainty of both primary and secondary fuzziness, in order to address regression problems, we built a type-2 Linguistic Random Regression Model based on credibility theory. Confidence intervals are constructed for fuzzy input and output, and the proposed regression model give a rise to a nonlinear programming problem focus on a well-trained model, which would be helpful and useful in linguistic assessment cases. Finally, a numerical example is provided.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2376-2383
    Number of pages8
    ISBN (Print)9781479920723
    DOIs
    Publication statusPublished - 2014 Sep 4
    Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing
    Duration: 2014 Jul 62014 Jul 11

    Other

    Other2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
    CityBeijing
    Period14/7/614/7/11

    Keywords

    • Confidence interval
    • Creditability theory
    • Linguistic rules
    • Regression model
    • Type-2 fuzzy set

    ASJC Scopus subject areas

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

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  • Cite this

    Song, F., Imai, S., & Watada, J. (2014). Building linguistic random regression model from the perspective of type-2 fuzzy set. In IEEE International Conference on Fuzzy Systems (pp. 2376-2383). [6891658] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FUZZ-IEEE.2014.6891658