Affiliations: [a] School of Artificial Intelligence, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China | [b] Key Laboratory of Complex System Optimization and Big Data Processing inDepartment of Guangxi Education, Yulin Normal University, Yulin, Guangxi, P.R. China | [c] Institute of Artificial Intelligence, School of Information Science and Technology, Southwest Jiaotong University, Chengdu, P.R. China
Correspondence:
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Corresponding author. Zhaowen Li, Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, Guangxi 537000, P.R. China. E-mail: lizhaowen8846@126.com.
Abstract: An information system as a database that stands for relationships between objects and attributes is an important mathematical model. An image information system is an information system where each of its information values is an image and its information structures embody internal features of this type of information system. Uncertainty measurement is an effective tool for evaluation. This paper explores measures of uncertainty for an information system by using the proposed information structures. The distance between two objects in an image information system is first given. After that, the fuzzy Tcos-equivalence relation, induced by this system by using Gaussian kernel method, is obtained, where Gaussian kernel is based on this distance. Next, information structures of this system are described by set vectors, dependence between information structures is studied and properties of information structures are given by using inclusion degree, and application for information structures and uncertainty measures of an image information system are investigated by the information structures. Moreover, effectiveness analysis is done to show the feasibility of the proposed measures from the angle of statistics. Finally, an application of the proposed measurement for attribute reduction is given. These results will be helpful for understanding the essence of uncertainty in an image information system.
Keywords: Granular computing, image information system, distance, information structure, dependence, inclusion degree, uncertainty, measure
