Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method
A set-valued information system (SVIS) with missing values is known as an incomplete set-valued information system (ISVIS). This article focuses on studying uncertainty measurement for an ISVIS and the optimal selection of subsystems by means of Gaussian kernel. First, the distance between two infor...
| Main Authors: | , , , , , |
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| Format: | Article |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9266028/ |
| _version_ | 1818725756081012736 |
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| author | Lijun Chen Shimin Liao Ningxin Xie Zhaowen Li Gangqiang Zhang Ching-Feng Wen |
| author_facet | Lijun Chen Shimin Liao Ningxin Xie Zhaowen Li Gangqiang Zhang Ching-Feng Wen |
| author_sort | Lijun Chen |
| collection | DOAJ |
| description | A set-valued information system (SVIS) with missing values is known as an incomplete set-valued information system (ISVIS). This article focuses on studying uncertainty measurement for an ISVIS and the optimal selection of subsystems by means of Gaussian kernel. First, the distance between two information values on each attribute in an ISVIS is put forward. Second, the fuzzy $T_{cos}$ -equivalence relation induced by a given subsystem is proposed based on Gaussian kernel. Next, some tools are used to measure the uncertainty of an ISVIS. Moreover, effectiveness analysis is done from a statistical point of view. In the end, the optimal selection of subsystems based on $\delta $ -information granulation and $\delta $ -information amount is given. These results will help us comprehend nature of uncertainty in an ISVIS. |
| first_indexed | 2024-12-17T21:47:22Z |
| format | Article |
| id | doaj.art-e93ea53c33f5445f9227487b99d148ee |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T21:47:22Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e93ea53c33f5445f9227487b99d148ee2022-12-21T21:31:26ZengIEEEIEEE Access2169-35362020-01-01821202221203510.1109/ACCESS.2020.30397789266028Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel MethodLijun Chen0Shimin Liao1Ningxin Xie2https://orcid.org/0000-0002-4291-8181Zhaowen Li3https://orcid.org/0000-0002-4437-9214Gangqiang Zhang4https://orcid.org/0000-0001-5424-6389Ching-Feng Wen5School of Mathematics and Statistics, Yulin Normal University, Yulin, ChinaSchool of Artificial Intelligence, Guangxi University for Nationalities, Nanning, ChinaSchool of Artificial Intelligence, Guangxi University for Nationalities, Nanning, ChinaDepartment of Guangxi Education, Key Laboratory of Complex System Optimization and Big Data Processing, Yulin Normal University, Yulin, ChinaSchool of Artificial Intelligence, Guangxi University for Nationalities, Nanning, ChinaCenter for Fundamental Science, Kaohsiung Medical University, Kaohsiung, TaiwanA set-valued information system (SVIS) with missing values is known as an incomplete set-valued information system (ISVIS). This article focuses on studying uncertainty measurement for an ISVIS and the optimal selection of subsystems by means of Gaussian kernel. First, the distance between two information values on each attribute in an ISVIS is put forward. Second, the fuzzy $T_{cos}$ -equivalence relation induced by a given subsystem is proposed based on Gaussian kernel. Next, some tools are used to measure the uncertainty of an ISVIS. Moreover, effectiveness analysis is done from a statistical point of view. In the end, the optimal selection of subsystems based on $\delta $ -information granulation and $\delta $ -information amount is given. These results will help us comprehend nature of uncertainty in an ISVIS.https://ieeexplore.ieee.org/document/9266028/ISVISdistanceGaussian kernel<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Tcos</italic>-equivalence relationmeasureeffectiveness analysis |
| spellingShingle | Lijun Chen Shimin Liao Ningxin Xie Zhaowen Li Gangqiang Zhang Ching-Feng Wen Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method IEEE Access ISVIS distance Gaussian kernel <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Tcos</italic>-equivalence relation measure effectiveness analysis |
| title | Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method |
| title_full | Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method |
| title_fullStr | Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method |
| title_full_unstemmed | Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method |
| title_short | Measures of Uncertainty for an Incomplete Set-Valued Information System With the Optimal Selection of Subsystems: Gaussian Kernel Method |
| title_sort | measures of uncertainty for an incomplete set valued information system with the optimal selection of subsystems gaussian kernel method |
| topic | ISVIS distance Gaussian kernel <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Tcos</italic>-equivalence relation measure effectiveness analysis |
| url | https://ieeexplore.ieee.org/document/9266028/ |
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