Uncertainty measurement for a covering information system
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A covering information system as the generalization of an information system is an important model in the field of artificial intelligence. Uncertainty measurement is a critical evaluating tool. This paper investigates uncertainty measurement for a covering information system. The concept of information structures in a covering information system is first described by using set vectors. Then, dependence between information structures in a covering information system is introduced. Next, the axiom definition of granularity measure of uncertainty for covering information systems is proposed by means of its information structures, and based on this axiom definition, information granulation and rough entropy in a covering information system are proposed. Moreover, information entropy and information amount in a covering information system are also considered. Finally, we conduct a numerical experiment on the congressional voting records data set that comes from UCI Repository of machine learning databases, and based on this numerical experiment, effectiveness analysis from the angle of statistics is given to evaluate the performance of uncertainty measurement for a covering information system. These results will be helpful for understanding the essence of uncertainty in a covering information system.
KeywordsCovering information system Information granule Information structure Uncertainty Measurement Entropy Granularity Experiment Effectiveness
The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions which have helped immensely in improving the quality of the paper. This work is supported by National Natural Science Foundation of China (11461005), Natural Science Foundation of Guangxi (2016GXNSFAA380045, 2016GXNSFAA380282, 2016GXNSFAA38 0286), Key Laboratory of Optimization Control and Engineering Calculation in Department of Guangxi Education and Special Funds of Guangxi Distinguished Experts Construction Engineering.
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Conflict of Interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Beaubouef T, Petry FE, Arora G (1988) Information-theoretic measures of uncertainty for rough sets and rough relational databases. Inf Sci 109(1–4):185–195Google Scholar
- Lin TY (1998) Granular computing on binary relations I: data mining and neighborhood systems. In: Skowron A, Polkowski L (eds) Rough sets in knowledge discovery. Physica-Verlag, Heidelberg, pp 107–121Google Scholar
- Lin TY (1998) Granular computing on binary relations II: rough set representations and belief functions. In: Skowron A, Polkowski L (eds) Rough sets in knowledge discovery. Physica-Verlag, Heidelberg, pp 121–140Google Scholar