Abstract
In this study, based on the principle of justifiable granularity, a method for designing multi-dimensional information granules is proposed. To design information granules, the correlations among the different variables within the data and their confidence levels are considered. The designed information granules reveal the relationships present in the experimental data and help to capture more features of the original data. In addition, a strategy for the exclusion of inhibitory data is considered, making the design of information granules more focused. Several experimental studies are conducted to quantify the effectiveness of the proposed method.
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