Regression model based on fuzzy random variables

Shinya Imai, Shuming Wang, Junzo Watada

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

    4 Citations (Scopus)

    Abstract

    In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems. The objective of this paper is to build a regression model based on fuzzy random variables. First, a general regression model for fuzzy random data is proposed. After that, using expected value operators of fuzzy random variables, an expected regression model is established. The expected regression model can be developed by converting the original problem to a task of a linear programming problem. Finally, an explanatory example is provided.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages127-135
    Number of pages9
    Volume5179 LNAI
    EditionPART 3
    DOIs
    Publication statusPublished - 2008
    Event12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb
    Duration: 2008 Sep 32008 Sep 5

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 3
    Volume5179 LNAI
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008
    CityZagreb
    Period08/9/308/9/5

    Keywords

    • Expected value
    • Fuzzy random variable
    • Fuzzy regression model

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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  • Cite this

    Imai, S., Wang, S., & Watada, J. (2008). Regression model based on fuzzy random variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 5179 LNAI, pp. 127-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5179 LNAI, No. PART 3). https://doi.org/10.1007/978-3-540-85567-5-17