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Multigranulation super-trust model for attribute reduction

Ding, W, Pedrycz, W, Triguero, I, Cao, Z ORCID: 0000-0003-3656-0328 and Lin, CT 2020 , 'Multigranulation super-trust model for attribute reduction' , IEEE Transactions on Fuzzy Systems , pp. 1-14 , doi: 10.1109/TFUZZ.2020.2975152.

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Abstract

As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists' preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables

Item Type: Article
Authors/Creators:Ding, W and Pedrycz, W and Triguero, I and Cao, Z and Lin, CT
Keywords: multigranulation super-trust model, fuzzy- rough attribute reduction, valued tolerance relation, ensemble consensus compensatory scheme
Journal or Publication Title: IEEE Transactions on Fuzzy Systems
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
ISSN: 1063-6706
DOI / ID Number: 10.1109/TFUZZ.2020.2975152
Copyright Information:

Copyright 2020 IEEE

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