Information structures and uncertainty in an image information system

2021 ◽  
Vol 40 (1) ◽  
pp. 295-317
Author(s):  
Gangqiang Zhang ◽  
Zhaowen Li ◽  
Pengfei Zhang ◽  
Ningxin Xie
Keyword(s):  
Information System ◽  
Kernel Method ◽  
Attribute Reduction ◽  
Gaussian Kernel ◽  
Information Structures ◽  
Image Information ◽  
Effectiveness Analysis ◽  
Uncertainty Measurement ◽  
Properties Of Information ◽  
Information Values

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.

scholarly journals Uncertainty Measurement for a Set-Valued Information System: Gaussian Kernel Method

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 199 ◽  
Author(s):  
Jiali He ◽  
Pei Wang ◽  
Zhaowen Li
Keyword(s):  
Information System ◽  
Equivalence Relation ◽  
Kernel Method ◽  
Gaussian Kernel ◽  
Intrinsic Properties ◽  
Information Structures ◽  
Effectiveness Analysis ◽  
Uncertainty Measurement ◽  
Properties Of Information

A set-valued information system (SIS) is the generalization of a single-valued informationsystem. This article explores uncertainty measurement for a SIS by using Gaussian kernel. The fuzzyTcos-equivalence relation lead by a SIS is first obtained by using Gaussian kernel. Then, informationstructures in this SIS are described by set vectors. Next, dependence between information structuresis presented and properties of information structures are investigated. Lastly, uncertainty measuresof a SIS are presented by using its information structures. Moreover, effectiveness analysis is doneto assess the feasibility of our presented measures. The consequence of this article will help usunderstand the intrinsic properties of uncertainty in a SIS.

scholarly journals Measures of Uncertainty for a Four-Hybrid Information System and Their Applications

2021 ◽  
Author(s):  
Bin Qin ◽  
Fanping Zeng ◽  
Kesong Yan
Keyword(s):  
Information System ◽  
Missing Data ◽  
Distance Function ◽  
Information Structure ◽  
Attribute Reduction ◽  
Information Structures ◽  
Tolerance Relation ◽  
Effectiveness Analysis ◽  
Entropy Measures

Abstract A four-hybrid information system (4HIS) is an information system (IS) where the dataset of object descriptions consists of categorical, boolean, real-valued and missing data or attributes. This paper studies measures of uncertainty for a 4HIS and its application in attribute reduction. The distance function for each type of attribute in a 4HIS is first provided. Then, this distance is used to produce the tolerance relation induced by a given subsystem in a 4HIS. Next, information structure of this subsystem is proposed in terms of a set vector and dependence between information structures is introduced. Moreover, granulation and entropy measures in a 4HIS are investigated on the basis of information structures. In order to verify the feasibility of the proposed measures, effectiveness analysis is performed from a statistical perspective. Finally, an application of the proposed measures for attribute reduction in a 4HIS is given.

scholarly journals Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 212022-212035
Author(s):  
Lijun Chen ◽  
Shimin Liao ◽  
Ningxin Xie ◽  
Zhaowen Li ◽  
Gangqiang Zhang ◽  
...  
Keyword(s):  
Information System ◽  
Kernel Method ◽  
Gaussian Kernel ◽  
Optimal Selection ◽  
Selection Of

Feature selection for incomplete set-valued data

2021 ◽  
pp. 1-19
Author(s):  
Lulu Li
Keyword(s):  
Information System ◽  
Feature Selection ◽  
Practical Significance ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Uncertainty Measurement ◽  
Selection For ◽  
Average Size ◽  
Selection Algorithms ◽  
Information Values

Set-valued data is a significant kind of data, such as data obtained from different search engines, market data, patients’ symptoms and behaviours. An information system (IS) based on incomplete set-valued data is called an incomplete set-valued information system (ISVIS), which generalized model of a single-valued incomplete information system. This paper gives feature selection for an ISVIS by means of uncertainty measurement. Firstly, the similarity degree between two information values on a given feature of an ISVIS is proposed. Then, the tolerance relation on the object set with respect to a given feature subset in an ISVIS is obtained. Next, λ-reduction in an ISVIS is presented. What’s more, connections between the proposed feature selection and uncertainty measurement are exhibited. Lastly, feature selection algorithms based on λ-discernibility matrix, λ-information granulation, λ-information entropy and λ-significance in an ISVIS are provided. In order to better prove the practical significance of the provided algorithms, a numerical experiment is carried out, and experiment results show the number of features and average size of features by each feature selection algorithm.

Some results on fuzzy relations

2021 ◽  
pp. 1-17
Author(s):  
Yini Wang ◽  
Sichun Wang
Keyword(s):  
Set Theory ◽  
Fuzzy Set Theory ◽  
Attribute Reduction ◽  
Heterogeneous Data ◽  
Fuzzy Relation ◽  
Fuzzy Relations ◽  
Main Research ◽  
Effectiveness Analysis ◽  
Uncertainty Measurement ◽  
Partial Dependence

Fuzzy relation is one of the main research contents of fuzzy set theory. This paper obtains some results on fuzzy relations by studying relationships between fuzzy relations and their uncertainty measurement. The concepts of equality, dependence, partial dependence and independence between fuzzy relations are first introduced. Then, uncertainty measurement for a fuzzy relation is investigated by using dependence between fuzzy relations. Moreover, the basic properties of uncertainty measurement are obtained. Next, effectiveness analysis is carried out. Finally, an application of the proposed measures in attribute reduction for heterogeneous data is given. These results will be helpful for understanding the essence of a fuzzy relation.

Uncertainty measurement for a fuzzy set-valued information system

2021 ◽  
Author(s):  
Zhaowen Li ◽  
Zhihong Wang ◽  
Qingguo Li ◽  
Pei Wang ◽  
Ching-Feng Wen
Keyword(s):  
Information System ◽  
Fuzzy Set ◽  
Uncertainty Measurement
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