A fuzzy random variable approach to restructuring of rough sets through statistical test

Junzo Watada*, Lee Chuan Lin, Minji Qiang, Pei Chun Lin

*Corresponding author for this work

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

    3 Citations (Scopus)

    Abstract

    Usually it is hard to classify the situation where randomness and fuzziness exist simultaneously. This paper presents a method based on fuzzy random variables and statistical t-test to restructure a rough set. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages269-277
    Number of pages9
    Volume5908 LNAI
    DOIs
    Publication statusPublished - 2009
    Event12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009 - Delhi
    Duration: 2009 Dec 152009 Dec 18

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5908 LNAI
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009
    CityDelhi
    Period09/12/1509/12/18

    Keywords

    • Fuzzy Random Variable
    • Fuzzy t-test
    • Restructuring
    • Rough Sets

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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