The classical rough set theory thinks knowledge is granular and defines the concept of knowledge roughness, but it can not completely distinguish the information from the different knowledge granularity.
The classical rough set theory thinks knowledge is granular and defines the concept of knowledge roughness,but it can not completely distinguish the information from the different knowledge granularity.
The basic idea is that, after selecting core attributes as the root of the decision tree, a branching node will then be generated if the corresponding condition attribute provides the smallest knowledge roughness.
Two methods of knowledge discovery based on rough set (FCKD and SCKD), unlike those based on reduction of attributes, are presented in this paper. Their function is to find new determinative attributes which the original rules do not possess by reducing vagueness of the rough set when information system has varied dynamically. The structure of attributes is accordingly re constructed and new rules formed. Two operators for finding new attributes are defined, two theorems and an inference formulated. Exampl...
We propose a new approach to multivariate decision tree construction based on knowledge roughness in rough set instead of information entropy as usual. The basic idea is that, after selecting core attributes as the root of the decision tree, a branching node will then be generated if the corresponding condition attribute provides the smallest knowledge roughness.
Rough set theory is a relatively new soft computing technique and has become a topic of general interest in the field of knowledge discovery and decision-making. The classical rough set theory thinks knowledge is granular and defines the concept of knowledge roughness, but it can not completely distinguish the information from the different knowledge granularity. From the information theory, the concept of information granularity is defined. Granularity principle of knowledge roughness is studied stressfull...