Measuring Uncertainty in Neighborhood Information System
Measuring uncertainty of information system plays an important role in rough set theory. Shannons information entropy is an effective tool for measuring uncertainty in information system and it has been successfully applied to measure uncertainty of different systems in rough set theory. However, previous studies are only for classical rough set theory which can only deal with nominal attributes. Neighborhood rough set is a more comprehensive model which can handle numerical attributes and nominal attributes simultaneously. Some basic knowledge about neighborhood rough set is firstly studied in this paper. Neighborhood information entropy, neighborhood conditional information entropy and a measure of neighborhood mutual information are introduced respectively. Some of their important properties are also given. These results will be very helpful for understanding the essence of knowledge content and uncertainty measurement in neighborhood information systems.
Keywords: Neighborhood information system; Uncertainty; Entropy; Mutual information; Rough set theory
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ABOUT THE AUTHORS
Si-Yuan Jing
Sichuan Province University Key Laboratory of Internet Natural Language Intelligent Processing, Leshan Normal University
Kun She
School of Computer Science and Engineering, University of Electronic Science and Technology of China
Si-Yuan Jing
Sichuan Province University Key Laboratory of Internet Natural Language Intelligent Processing, Leshan Normal University
Kun She
School of Computer Science and Engineering, University of Electronic Science and Technology of China
