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Self-Organizing Fuzzy Belief Inference System for Classification

Gu, Xiaowei, Angelov, Plamen, Shen, Qiang (2022) Self-Organizing Fuzzy Belief Inference System for Classification. IEEE Transactions on Fuzzy Systems, . ISSN 1063-6706. (doi:10.1109/TFUZZ.2022.3179148) (KAR id:95205)

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Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.

Item Type: Article
DOI/Identification number: 10.1109/TFUZZ.2022.3179148
Uncontrolled keywords: belief structure, classification, data streams, evolving fuzzy system, fuzzy belief rule
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Xiaowei Gu
Date Deposited: 27 May 2022 13:59 UTC
Last Modified: 31 Aug 2022 13:38 UTC
Resource URI: (The current URI for this page, for reference purposes)
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