Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System
An information system is a database that represents relationships between objects and attributes. A set-valued information system is the generalized model of a single-valued information system. A set-value information system that contains probability distributions and missing values is called an inc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8638506/ |
| _version_ | 1818618694350143488 |
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| author | Xiaoliang Xie Zhaowen Li Pengfei Zhang Gangqiang Zhang |
| author_facet | Xiaoliang Xie Zhaowen Li Pengfei Zhang Gangqiang Zhang |
| author_sort | Xiaoliang Xie |
| collection | DOAJ |
| description | An information system is a database that represents relationships between objects and attributes. A set-valued information system is the generalized model of a single-valued information system. A set-value information system that contains probability distributions and missing values is called an incomplete probability set-value information system (IPSIS). Uncertainty measure is an effective tool for evaluation. This paper explores information structures and uncertainty measures in an IPSIS. According to the Bhattacharyya distance, the distance between two objects in a given subsystem of an IPSIS is first proposed. Then, the tolerance relation on an object set, induced by a probability set-valued information system by using this distance, is obtained. Next, the information structure of this subsystem is introduced by a set vector. Moreover, the dependence between two information structures is studied by using the inclusion degree. Finally, as an application for information structures, measures of uncertainty for an IPSIS are investigated, and to evaluate the performance of the proposed measures, effectiveness analysis is given from the angle of statistics. These results will be helpful for establishing a framework of granular computing and understanding the essence of uncertainty in an IPSIS. |
| first_indexed | 2024-12-16T17:25:40Z |
| format | Article |
| id | doaj.art-e9340d003bbc41aa8e1ce49d788561d8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:25:40Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e9340d003bbc41aa8e1ce49d788561d82022-12-21T22:23:03ZengIEEEIEEE Access2169-35362019-01-017275012751410.1109/ACCESS.2019.28977528638506Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information SystemXiaoliang Xie0Zhaowen Li1https://orcid.org/0000-0002-4437-9214Pengfei Zhang2Gangqiang Zhang3Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, School of Mathematics and Statistics, Institute of Big Data and Internet Innovation, Hunan University of Commerce, Changsha, ChinaDepartment of Guangxi Education, Key Laboratory of Complex System Optimization and Big Data Processing, Yulin Normal University, Yulin, ChinaSchool of Science, Guangxi University for Nationalities, Nanning, ChinaSchool of Software and Information Security, Guangxi University for Nationalities, Nanning, ChinaAn information system is a database that represents relationships between objects and attributes. A set-valued information system is the generalized model of a single-valued information system. A set-value information system that contains probability distributions and missing values is called an incomplete probability set-value information system (IPSIS). Uncertainty measure is an effective tool for evaluation. This paper explores information structures and uncertainty measures in an IPSIS. According to the Bhattacharyya distance, the distance between two objects in a given subsystem of an IPSIS is first proposed. Then, the tolerance relation on an object set, induced by a probability set-valued information system by using this distance, is obtained. Next, the information structure of this subsystem is introduced by a set vector. Moreover, the dependence between two information structures is studied by using the inclusion degree. Finally, as an application for information structures, measures of uncertainty for an IPSIS are investigated, and to evaluate the performance of the proposed measures, effectiveness analysis is given from the angle of statistics. These results will be helpful for establishing a framework of granular computing and understanding the essence of uncertainty in an IPSIS.https://ieeexplore.ieee.org/document/8638506/Granular computingincomplete probability set-valued information systemBhattacharyya distanceinformation structuredependenceuncertainty |
| spellingShingle | Xiaoliang Xie Zhaowen Li Pengfei Zhang Gangqiang Zhang Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System IEEE Access Granular computing incomplete probability set-valued information system Bhattacharyya distance information structure dependence uncertainty |
| title | Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System |
| title_full | Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System |
| title_fullStr | Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System |
| title_full_unstemmed | Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System |
| title_short | Information Structures and Uncertainty Measures in an Incomplete Probabilistic Set-Valued Information System |
| title_sort | information structures and uncertainty measures in an incomplete probabilistic set valued information system |
| topic | Granular computing incomplete probability set-valued information system Bhattacharyya distance information structure dependence uncertainty |
| url | https://ieeexplore.ieee.org/document/8638506/ |
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