A real-time analysis of granular information: Some initial thoughts on a convex hull-based fuzzy regression approach

Azizul Azhar Ramli, Witold Pedrycz, Junzo Watada, Nureize Arbaiy

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

    Abstract

    Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    Pages2851-2858
    Number of pages8
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei
    Duration: 2011 Jun 272011 Jun 30

    Other

    Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
    CityTaipei
    Period11/6/2711/6/30

    Fingerprint

    Information granules
    Fuzzy Regression
    Convex Hull
    Granular computing
    Real-time
    System Modeling
    Clustering algorithms
    Regression analysis
    Linear programming
    Computational complexity
    Data analysis
    Genetic algorithms
    Granular Computing
    Fuzzy C-means
    Regression Analysis
    Clustering Algorithm
    Regression Model
    Computational Complexity
    Genetic Algorithm
    Computing

    Keywords

    • convex hull
    • Fuzzy C-Means
    • fuzzy regression
    • genetic algorithm
    • granular computing

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science

    Cite this

    Ramli, A. A., Pedrycz, W., Watada, J., & Arbaiy, N. (2011). A real-time analysis of granular information: Some initial thoughts on a convex hull-based fuzzy regression approach. In IEEE International Conference on Fuzzy Systems (pp. 2851-2858). [6007429] https://doi.org/10.1109/FUZZY.2011.6007429

    A real-time analysis of granular information : Some initial thoughts on a convex hull-based fuzzy regression approach. / Ramli, Azizul Azhar; Pedrycz, Witold; Watada, Junzo; Arbaiy, Nureize.

    IEEE International Conference on Fuzzy Systems. 2011. p. 2851-2858 6007429.

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

    Ramli, AA, Pedrycz, W, Watada, J & Arbaiy, N 2011, A real-time analysis of granular information: Some initial thoughts on a convex hull-based fuzzy regression approach. in IEEE International Conference on Fuzzy Systems., 6007429, pp. 2851-2858, 2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011, Taipei, 11/6/27. https://doi.org/10.1109/FUZZY.2011.6007429
    Ramli AA, Pedrycz W, Watada J, Arbaiy N. A real-time analysis of granular information: Some initial thoughts on a convex hull-based fuzzy regression approach. In IEEE International Conference on Fuzzy Systems. 2011. p. 2851-2858. 6007429 https://doi.org/10.1109/FUZZY.2011.6007429
    Ramli, Azizul Azhar ; Pedrycz, Witold ; Watada, Junzo ; Arbaiy, Nureize. / A real-time analysis of granular information : Some initial thoughts on a convex hull-based fuzzy regression approach. IEEE International Conference on Fuzzy Systems. 2011. pp. 2851-2858
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