Building a Type II Fuzzy Qualitative Regression Model

  • Yicheng Wei
  • Junzo Watada
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)


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.


Type II fuzzy qualitative regression model quantification type I fuzzy set type II fuzzy set type I fuzzy number type II fuzzy number linear programming LP 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushuJapan

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