Abstract
An alternative perspective on granular modelling is introduced where an information granule characterises the relationship between a label expression and elements in an underlying perceptual space. Label semantics is proposed as a framework for representing information granules of this kind. Mass relations and linguistic decision trees are then introduced as two types of granular models in label semantics. Finally, its shown how linguistic decision trees can be combined within an attribute hierarchy to model complex multi-level composite mappings.
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