Building a type II fuzzy qualitative regression model

Yicheng Wei*, Junzo Watada

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapter

    10 Citations (Scopus)


    The qualitative regression analysis models quantitatively change in the qualitative object variables by using qualitative values of multivariate data (membership degree or type I fuzzy set), which are given by subjective recognitions and judgments. From fuzzy set-theoretical points of view, uncertainty also exists when associated with the membership function of a type I fuzzy set. It will have much impact on the fuzziness of the qualitative objective external criterion. This paper is trying to model the qualitative change of external criterion's fuzziness by applying type II fuzzy set (we will use type II fuzzy set as well as type II fuzzy data in this paper). Here, qualitative values are assumed to be fuzzy degree of membership in qualitative categories and qualitative change in the objective external criterion is given as the fuzziness of the output.

    Original languageEnglish
    Title of host publicationSmart Innovation, Systems and Technologies
    Number of pages10
    Publication statusPublished - 2012

    Publication series

    NameSmart Innovation, Systems and Technologies
    ISSN (Print)21903018
    ISSN (Electronic)21903026


    • Linear programming
    • LP
    • Quantification
    • Type I fuzzy number
    • Type I fuzzy set
    • Type II fuzzy number
    • Type II fuzzy qualitative regression model
    • Type II fuzzy set

    ASJC Scopus subject areas

    • Computer Science(all)
    • Decision Sciences(all)


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