Self-Organizing Fuzzy Belief Inference System for Classification

Gu, Xiaowei and Angelov, Plamen and Shen, Qiang (2022) Self-Organizing Fuzzy Belief Inference System for Classification. IEEE Transactions on Fuzzy Systems, 30 (12). pp. 5473-5483. ISSN 1063-6706

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Abstract

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:
Journal Article
Journal or Publication Title:
IEEE Transactions on Fuzzy Systems
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2207
Subjects:
ID Code:
171982
Deposited By:
Deposited On:
17 Jun 2022 13:55
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Jan 2023 02:06