Multigranulation Super-Trust Model for Attribute Reduction

Ding, Weiping; Pedrycz, Witold; Triguero, Isaac; Cao, Zehong; Lin, Chin-Teng

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 article, we present a novel multigranulation supertrust 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 supertrust 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 was 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 this strategy 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.

Citation

Ding, W., Pedrycz, W., Triguero, I., Cao, Z., & Lin, C.-T. (2020). Multigranulation Super-Trust Model for Attribute Reduction. IEEE Transactions on Fuzzy Systems, 29(6), 1395-1408. https://doi.org/10.1109/tfuzz.2020.2975152

• Journal Article Type: Article
• Acceptance Date: Feb 11, 2020
• Online Publication Date: Feb 19, 2020
• Publication Date: Feb 19, 2020
• Deposit Date: Mar 4, 2020
• Publicly Available Date: Mar 4, 2020
• Journal: IEEE Transactions on Fuzzy Systems
• Print ISSN: 1063-6706
• Electronic ISSN: 1941-0034
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 6
• Pages: 1395-1408
• DOI: https://doi.org/10.1109/tfuzz.2020.2975152
• Keywords: Control and Systems Engineering; Computational Theory and Mathematics; Applied Mathematics; Artificial Intelligence
• Public URL: https://nottingham-repository.worktribe.com/output/4089650
• Publisher URL: https://ieeexplore.ieee.org/document/9003225
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.