Reduction in a fuzzy probability information system based on incomplete set-valued data

Attribute reduction for incomplete data is a hot topic in rough set theory (RST). A fuzzy probabilistic information system (FPIS) combines of fuzzy relations that satisfy the probability distribution about objects, which can be regarded as an information system (IS) with fuzzy relations. This paper...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 45; no. 3; pp. 3749 - 3765
Main Authors Li, Zhaowen, Luo, Damei, Yu, Guangji
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 24.08.2023
Subjects
Algorithms
Cluster analysis
Clustering
Entropy (Information theory)
Fuzzy set theory
Granulation
Reduction
Rough set models
Statistical analysis
Vector quantization
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Summary:Attribute reduction for incomplete data is a hot topic in rough set theory (RST). A fuzzy probabilistic information system (FPIS) combines of fuzzy relations that satisfy the probability distribution about objects, which can be regarded as an information system (IS) with fuzzy relations. This paper studies attribute reduction in an FPIS. Based on the available information of objects on an ISVIS, the probability distribution formula of objects is first defined. Then, an FPIS can be induced by an ISVIS. Next, attribute reduction in a FPIS is proposed similar to an IS. Moreover, information granulation and information entropy in an FPIS is defined, and the corresponding algorithms are constructed. Finally, the effectiveness of the constructed algorithms is verified by k-means clustering, Friedman test and Nemenyi test.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-230865