Affiliations: [a] School of Computer Science, Guangdong University of Science and Technology, Dongguan, Guangdong, P.R. China
| [b] Qingxi Middle School, Dongguan Senior High School Group, Dongguan, Guangdong, P.R. China
| [c] School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning, Guangxi, P.R. China
Correspondence:
[*]
Corresponding author. GuangjiYu, School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning, Guangxi 530003, P.R. China. E-mail: [email protected].
Abstract: 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.
