Uncertainty Measures in Interval-Valued Information Systems
Rough set theory is a new mathematical tool to deal with vagueness and uncertainty in artificial intelligence. Approximation accuracy, knowledge granularity and entropy theory are three main approaches to uncertainty research in classical Pawlak information system, which have been widely applied in many practical issues. Based on uncertainty measures in Pawlak information systems, we propose rough degree, knowledge discernibility and rough entropy in interval-valued information systems, and investigate some important properties of them. Finally, the relationships between knowledge granulation, knowledge discerniblity and rough degree have been also discussed.
KeywordsUpper and lower approximations rough sets uncertainty measures
Unable to display preview. Download preview PDF.
- 6.Qian, J., Miao, D.Q., Zhang, Z.H.: Knowledge reduction algorithms in cloud computing. Chinese Journal of Computers 12, 2332–2343 (2011)Google Scholar
- 12.Huang, B., Zhou, X.Z., Shi, Y.C.: Entropy of knowledge and rough set based on general binary relation. Journal of Systems Engineering: theory and Practice 24, 93–96 (2004)Google Scholar
- 16.Zhang, N., Miao, D.Q., Yue, X.D.: Knowledge reduction in interval-valued information systems. Chinese Journal of Computer Research and Development 47, 1362–1371 (2010)Google Scholar
- 17.Zhang, N.: Research on Interval-valued Information Systems and Knowledge Spaces: A Granular Approach. PhD Thesis, Tongji University, Shanghai, China (2012)Google Scholar