Title
Uncertainty measurement for a covering information system
Abstract
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.
Year
DOI
Venue
2019
10.1007/s00500-018-3458-5
soft computing
Keywords
Field
DocType
Covering information system, Information granule, Information structure, Uncertainty, Measurement, Entropy, Granularity, Experiment, Effectiveness
Information system,Information structure,Data mining,Voting,Axiom,Computer science,Measurement uncertainty,Artificial intelligence,Granularity,Entropy (information theory),Machine learning
Journal
Volume
Issue
ISSN
23.0
14.0
1433-7479
Citations 
PageRank 
References 
1
0.35
33
Authors
5
Name
Order
Citations
PageRank
Zhaowen Li113616.20
pengfei zhang23213.28
Xun Ge361.06
Ningxin Xie4698.08
Gangqiang Zhang5468.63