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    Area of Science:

    • Data Science
    • Artificial Intelligence
    • Fuzzy Logic

    Background:

    • Incomplete data presents significant challenges in data processing and analysis.
    • Existing data imputation and modeling methods often rely on numeric approaches and require pre-defined numeric models.
    • Granular Computing (GrC) offers a higher level of abstraction to address data incompleteness.

    Purpose of the Study:

    • To introduce a novel and straightforward approach for handling incomplete data using information granules.
    • To develop granular fuzzy models directly from hybrid granular and numeric data.
    • To represent and process missing data effectively within a granular framework.

    Main Methods:

    • Utilizing information granules to represent missing data.
    • Building granular fuzzy models from hybrid granular and numeric data.
    • Employing the principle of justifiable granularity (coverage and specificity) for evaluation and optimization.
    • Leveraging particle swarm optimization for method refinement.

    Main Results:

    • Demonstrated the feasibility of the proposed method using synthetic and real-world datasets.
    • Analyzed the key features and performance of the granular fuzzy modeling approach for incomplete data.
    • Successfully integrated granular representations of missing data with numeric data.

    Conclusions:

    • The proposed granular computing approach offers an effective solution for handling incomplete data.
    • Granular fuzzy models can be directly built from hybrid data, overcoming limitations of traditional methods.
    • The method provides a flexible and robust framework for data imputation and modeling.