Reduction in a fuzzy probability information system based on incomplete set-valued data-Reference-Cited by-同舟云学术

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

Published:2023-08-24 Issue:3 Volume:45 Page:3749-3765
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Li Zhaowen¹,Luo Damei²,Yu Guangji³

Affiliation:

1. School of Computer Science, Guangdong University of Science and Technology, Dongguan, Guangdong, P.R. China

2. Qingxi Middle School, Dongguan Senior High School Group, Dongguan, Guangdong, P.R. China

3. School of Big Data and Artificial Intelligence, Guangxi University of Finance and Economics, Nanning, Guangxi, P.R. China

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.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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