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Synergistic Mechanism of Designing Information Granules with the Use of the Principle of Justifiable Granularity

Author

Listed:
  • Dan Wang

    (Institute of System Security and Control, School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Yukang Liu

    (Institute of System Security and Control, School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Zhenhua Yu

    (Institute of System Security and Control, School of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

The construction of information granules is a significant and interesting topic of Granular Computing (GrC) in which information granules play a vital role in representing and describing data, and it has become one of the most effective frameworks for solving complex problems. In this study, we are interested in the collaborative impacts of several different characteristics on constructing information granules, and a novel synergistic mechanism of the principle of justifiable granularity is utilized in developing information granules. The synergistic mechanism is finalized with a two-phase process—to start with, the principle of justifiable granularity and Fuzzy C-Means Clustering method are combined to develop a collection of information granules. First, the available experimental data is transformed (normalized) into fuzzy sets following the standard Fuzzy C-Means Clustering method. Then, information granules are developed based on the elements located in different clusters with the use of the principle of justifiable granularity. In the sequel, the positions of information granules are updated by considering the collaborative impacts of the other information granules with the parameters of specifying the level of influence. Experimental studies are conducted to illustrate the nature and feasibility of the proposed framework based on the synthetic data as well as a series of publicly available datasets coming from KEEL machine learning repositories.

Suggested Citation

  • Dan Wang & Yukang Liu & Zhenhua Yu, 2023. "Synergistic Mechanism of Designing Information Granules with the Use of the Principle of Justifiable Granularity," Mathematics, MDPI, vol. 11(7), pages 1-19, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1750-:d:1117227
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    References listed on IDEAS

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    1. Zhenhua Yu & Xudong Duan & Xuya Cong & Xiangning Li & Li Zheng, 2023. "Detection of Actuator Enablement Attacks by Petri Nets in Supervisory Control Systems," Mathematics, MDPI, vol. 11(4), pages 1-23, February.
    2. Pedrycz, Witold, 2014. "Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing," European Journal of Operational Research, Elsevier, vol. 232(1), pages 137-145.
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