Fuzzy robust regression models based on granularity and possibility distribution

Yoshiyuki Yabuuchi, Junzo Watada

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

    Abstract

    The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution. Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.

    Original languageEnglish
    Title of host publication2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1386-1391
    Number of pages6
    ISBN (Print)9781479959556
    DOIs
    Publication statusPublished - 2014 Feb 18
    Event2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 - Kitakyushu, Japan
    Duration: 2014 Dec 32014 Dec 6

    Other

    Other2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014
    CountryJapan
    CityKitakyushu
    Period14/12/314/12/6

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

    Cite this

    Yabuuchi, Y., & Watada, J. (2014). Fuzzy robust regression models based on granularity and possibility distribution. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 (pp. 1386-1391). [7044751] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCIS-ISIS.2014.7044751

    Fuzzy robust regression models based on granularity and possibility distribution. / Yabuuchi, Yoshiyuki; Watada, Junzo.

    2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1386-1391 7044751.

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

    Yabuuchi, Y & Watada, J 2014, Fuzzy robust regression models based on granularity and possibility distribution. in 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014., 7044751, Institute of Electrical and Electronics Engineers Inc., pp. 1386-1391, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014, Kitakyushu, Japan, 14/12/3. https://doi.org/10.1109/SCIS-ISIS.2014.7044751
    Yabuuchi Y, Watada J. Fuzzy robust regression models based on granularity and possibility distribution. In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1386-1391. 7044751 https://doi.org/10.1109/SCIS-ISIS.2014.7044751
    Yabuuchi, Yoshiyuki ; Watada, Junzo. / Fuzzy robust regression models based on granularity and possibility distribution. 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1386-1391
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