Using different criteria on balancing best granules (information pockets) are obtained.
In this context, fuzzy sets represent linguistic granules of information, contained in the antecedents and consequents of the rules, whereas the data used in the genetic learning is assumed to be crisp.
First, the lower and upper approximations of cost-sensitive multi-granulation are constructed, and it can be verified that in multi-granulation framework, the information granules and approximations are sensitive to decision costs and test costs, respectively.
Information granulation and resulting information granules themselves become an important design aspect of fuzzy models.
Furthermore, theories of transforming arbitrary information granule into necessary, sufficient, sufficient and necessary information granules are addressed carefully.
Each concept is one basic information granule, and the time range of information covered by each concept is called the window width of the granule.
In this case, the loss functions are determined in the form of interval-valued information granule.
In this study, we proposed two dynamic computing rough approximations approaches for time-evolving information granule interval-valued ordered information system which induced by the deletion or addition some attributes, respectively.
By analyzing the necessity and sufficient relations between the object and some of its attributes, the information granule concept is investigated in human cognitive processes.
This fact suggests that an appropriate quantification of the uncertainty expressed by the information granule model could be used to define an invariant property, to be exploited in practical situations of information granulation.
