A Granular Aggregation of Multifaceted Gaussian Process Models

This study focuses on the construction of granular Gaussian process models completed at different levels of granularity and the emergence of higher-type granular outputs through aggregating the individual prediction results. Each Gaussian process model is instantiated utilizing granular data (or information granules) to enhance algorithmic efficiency and can be tailored to specific levels of precision (granularity). The overall design methodology emphasizes human centricity in system modeling by focusing on both the interpretability and accuracy of the resulting models. First, clustering algorithms are applied to construct information granules that provide a comprehensive overview of the experimental evidence. As the number of information granules grows, the existing knowledge imbedded within data could be perceived and described at increased levels of details. Information granules are built in an augmented feature space constructed by concatenating the input and output variables. Next, Gaussian process models are constructed on a basis of the information granules formed at different levels of abstraction. Subsequently, the confidence intervals are transformed to intervals and the reconciliation of the predictions produced by individual models, which offer different perspectives on the system, leads to the emergence of more abstract entities (such as type-2 intervals/fuzzy sets, etc.) rather than plain numbers. The efficacy of the comprehensive model is measured by the coverage and specificity criteria of the granular outputs. Experimental studies conducted on a synthetic dataset and a number of real-world datasets validated the effectiveness and adaptability of the proposed methodology.