A convex hull-based fuzzy regression to information granules problem - An efficient solution to real-time data analysis

Azizul Azhar Ramli, Junzo Watada, Witold Pedrycz

    研究成果: Conference contribution

    抄録

    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-based Fuzzy C-Means (GA-FCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps 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 information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

    本文言語English
    ホスト出版物のタイトルCommunications in Computer and Information Science
    ページ190-204
    ページ数15
    180 CCIS
    PART 2
    DOI
    出版ステータスPublished - 2011
    イベント2nd International Conference on Software Engineering and Computer Systems, ICSECS 2011 - Kuantan
    継続期間: 2011 6 272011 6 29

    出版物シリーズ

    名前Communications in Computer and Information Science
    番号PART 2
    180 CCIS
    ISSN(印刷版)18650929

    Other

    Other2nd International Conference on Software Engineering and Computer Systems, ICSECS 2011
    CityKuantan
    Period11/6/2711/6/29

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)

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